Numerical representation for neural networks

ABSTRACT

Techniques in advanced deep learning provide improvements in one or more of accuracy, performance, and energy efficiency. An array of processing elements comprising a portion of a neural network accelerator performs flow-based computations on wavelets of data. Each processing element has a respective compute element and a respective routing element. Each compute element has a respective floating-point unit enabled to optionally and/or selectively perform floating-point operations in accordance with a programmable exponent bias and/or various floating-point computation variations. In some circumstances, the programmable exponent bias and/or the floating-point computation variations enable neural network processing with improved accuracy, decreased training time, decreased inference latency, and/or increased energy efficiency.

CROSS REFERENCE TO RELATED APPLICATIONS

To the extent permitted by the type of the instant application, thisapplication incorporates by reference for all purposes the followingapplications, all commonly owned with the instant application not laterthan the effective filing date of the instant application:

-   U.S. Non-Provisional application Ser. No. 17/262,717 (Docket No.    CS-17-19US), filed 2021 Jan. 23, issued 2021 Jul. 13 as U.S. patent    Ser. No. 11/062,202, first named inventor Sean LIE, and entitled    NUMERICAL REPRESENTATION FOR NEURAL NETWORKS;-   PCT Application Serial No PCT/IB2019/056118 (Docket No.    CS-17-19PCT), filed 2019 Jul. 17, first named inventor Sean LIE, and    entitled NUMERICAL REPRESENTATION FOR NEURAL NETWORKS;-   U.S. Provisional Application Ser. No. 62/703,014 (Docket No.    CS-17-19), filed 2018 Jul. 25, first named inventor Sean LIE, and    entitled NUMERICAL REPRESENTATION FOR NEURAL NETWORKS;-   PCT Application Serial No. PCT/IB2018/052667 (Docket No.    CS-17-05PCT), filed 2018 Apr. 17, first named inventor Sean LIE, and    entitled FABRIC VECTORS FOR DEEP LEARNING ACCELERATION;-   PCT Application Serial No. PCT/IB2018/052666 (Docket No.    CS-17-21PCT), filed 2018 Apr. 17, first named inventor Sean LIE, and    entitled BACKPRESSURE FOR ACCELERATED DEEP LEARNING;-   PCT Application Serial No. PCT/IB2018/052664 (Docket No.    CS-17-04PCT), filed 2018 Apr. 17, first named inventor Sean LIE, and    entitled CONTROL WAVELET FOR ACCELERATED DEEP LEARNING;-   PCT Application Serial No. PCT/IB2018/052651 (Docket No.    CS-17-22PCT), filed 2018 Apr. 17, first named inventor Sean LIE, and    entitled TASK ACTIVATING FOR ACCELERATED DEEP LEARNING;-   PCT Application Serial No. PCT/IB2018/052643 (Docket No.    CS-17-12PCT), filed 2018 Apr. 17, first named inventor Sean LIE, and    entitled DATA STRUCTURE DESCRIPTORS FOR DEEP LEARNING ACCELERATION;-   PCT Application Serial No. PCT/IB2018/052640 (Docket No.    CS-17-08PCT), filed 2018 Apr. 17, first named inventor Sean LIE, and    entitled MICROTHREADING FOR ACCELERATED DEEP LEARNING;-   PCT Application Serial No. PCT/IB2018/052638 (Docket No.    CS-17-06PCT), filed 2018 Apr. 16, first named inventor Sean LIE, and    entitled TASK SYNCHRONIZATION FOR ACCELERATED DEEP LEARNING;-   PCT Application Serial No. PCT/IB2018/052610 (Docket No.    CS-17-03PCT), filed 2018 Apr. 15, first named inventor Sean LIE, and    entitled WAVELET REPRESENTATION FOR ACCELERATED DEEP LEARNING;-   PCT Application Serial No. PCT/IB2018/052607 (Docket No.    CS-17-01PCT), filed 2018 Apr. 15, first named inventor Sean LIE, and    entitled NEURON SMEARING FOR ACCELERATED DEEP LEARNING;-   PCT Application Serial No. PCT/IB2018/052606 (Docket No.    CS-17-02PCT), filed 2018 Apr. 15, first named inventor Sean LIE, and    entitled DATAFLOW TRIGGERED TASKS FOR ACCELERATED DEEP LEARNING;-   PCT Application Serial No. PCT/IB2018/052602 (Docket No.    CS-17-11PCT), filed 2018 Apr. 13, first named inventor Sean LIE, and    entitled FLOATING-POINT UNIT STOCHASTIC ROUNDING FOR ACCELERATED    DEEP LEARNING;-   U.S. Provisional Application Ser. No. 62/655,826 (Docket No.    CS-17-08), filed 2018 Apr. 11, first named inventor Sean LIE, and    entitled MICROTHREADING FOR ACCELERATED DEEP LEARNING;-   U.S. Provisional Application Ser. No. 62/655,210 (Docket No.    CS-17-21), filed 2018 Apr. 9, first named inventor Sean LIE, and    entitled BACKPRESSURE FOR ACCELERATED DEEP LEARNING;-   U.S. Provisional Application Ser. No. 62/652,933 (Docket No.    CS-17-22), filed 2018 Apr. 5, first named inventor Sean LIE, and    entitled TASK ACTIVATING FOR ACCELERATED DEEP LEARNING;-   U.S. Non-Provisional application Ser. No. 15/903,340 (Docket No.    CS-17-13NP), filed 2018 Feb. 23, first named inventor Sean LIE, and    entitled ACCELERATED DEEP LEARNING;-   PCT Application Serial No. PCT/IB2018/051128 (Docket No.    CS-17-13PCT), filed 2018 Feb. 23, first named inventor Sean LIE, and    entitled ACCELERATED DEEP LEARNING;-   U.S. Provisional Application Ser. No. 62/628,784 (Docket No.    CS-17-05), filed 2018 Feb. 9, first named inventor Sean LIE, and    entitled FABRIC VECTORS FOR DEEP LEARNING ACCELERATION;-   U.S. Provisional Application Ser. No. 62/628,773 (Docket No.    CS-17-12), filed 2018 Feb. 9, first named inventor Sean LIE, and    entitled DATA STRUCTURE DESCRIPTORS FOR DEEP LEARNING ACCELERATION;-   U.S. Provisional Application Ser. No. 62/580,207 (Docket No.    CS-17-01), filed 2017 Nov. 1, first named inventor Sean LIE, and    entitled NEURON SMEARING FOR ACCELERATED DEEP LEARNING;-   U.S. Provisional Application Ser. No. 62/542,645 (Docket No.    CS-17-02), filed 2017 Aug. 8, first named inventor Sean LIE, and    entitled DATAFLOW TRIGGERED TASKS FOR ACCELERATED DEEP LEARNING;-   U.S. Provisional Application Ser. No. 62/542,657 (Docket No.    CS-17-06), filed 2017 Aug. 8, first named inventor Sean LIE, and    entitled TASK SYNCHRONIZATION FOR ACCELERATED DEEP LEARNING;-   U.S. Provisional Application Ser. No. 62/522,065 (Docket No.    CS-17-03), filed 2017 Jun. 19, first named inventor Sean LIE, and    entitled WAVELET REPRESENTATION FOR ACCELERATED DEEP LEARNING;-   U.S. Provisional Application Ser. No. 62/522,081 (Docket No.    CS-17-04), filed 2017 Jun. 19, first named inventor Sean LIE, and    entitled CONTROL WAVELET FOR ACCELERATED DEEP LEARNING;-   U.S. Provisional Application Ser. No. 62/520,433 (Docket No.    CS-17-13B), filed 2017 Jun. 15, first named inventor Michael Edwin    JAMES, and entitled INCREASED CONCURRENCY AND EFFICIENCY OF DEEP    NETWORK TRAINING VIA CONTINUOUS PROPAGATION;-   U.S. Provisional Application Ser. No. 62/517,949 (Docket No.    CS-17-14B), filed 2017 Jun. 11, first named inventor Sean LIE, and    entitled ACCELERATED DEEP LEARNING;-   U.S. Provisional Application Ser. No. 62/486,372 (Docket No.    CS-17-14), filed 2017 Apr. 17, first named inventor Sean LIE, and    entitled ACCELERATED DEEP LEARNING;-   U.S. Provisional Application Ser. No. 62/485,638 (Docket No.    CS-17-11), filed 2017 Apr. 14, first named inventor Sean LIE, and    entitled FLOATING-POINT UNIT STOCHASTIC ROUNDING FOR MACHINE    LEARNING; and-   U.S. Provisional Application Ser. No. 62/462,640 (Docket No.    CS-17-13), filed 2017 Feb. 23, first named inventor Michael Edwin    JAMES, and entitled INCREASED CONCURRENCY AND EFFICIENCY OF DEEP    NETWORK TRAINING VIA CONTINUOUS PROPAGATION.

BACKGROUND Field

Advancements in accelerated deep learning are needed to provideimprovements in one or more of accuracy, performance, and energyefficiency.

Related Art

Unless expressly identified as being publicly or well known, mentionherein of techniques and concepts, including for context, definitions,or comparison purposes, should not be construed as an admission thatsuch techniques and concepts are previously publicly known or otherwisepart of the prior art. All references cited herein (if any), includingpatents, patent applications, and publications, are hereby incorporatedby reference in their entireties, whether specifically incorporated ornot, for all purposes.

SYNOPSIS

The invention may be implemented in numerous ways, e.g., as a process,an article of manufacture, an apparatus, a system, a composition ofmatter, and a computer readable medium such as a computer readablestorage medium (e.g., media in an optical and/or magnetic mass storagedevice such as a disk, an integrated circuit having non-volatile storagesuch as flash storage), or a computer network wherein programinstructions are sent over optical or electronic communication links.The Detailed Description provides an exposition of one or moreembodiments of the invention that enable improvements in cost,profitability, performance, efficiency, and utility of use in the fieldidentified above. The Detailed Description includes an Introduction tofacilitate understanding of the remainder of the Detailed Description.The Introduction includes Example Embodiments of one or more of systems,methods, articles of manufacture, and computer readable media inaccordance with concepts described herein. As is discussed in moredetail in the Conclusions, the invention encompasses all possiblemodifications and variations within the scope of the issued claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates selected details of an embodiment of a system forneural network training and inference, using a deep learningaccelerator.

FIG. 2 illustrates selected details of an embodiment of softwareelements associated with neural network training and inference, using adeep learning accelerator.

FIG. 3 illustrates selected details of an embodiment of processingassociated with training a neural network and performing inference usingthe trained neural network, using a deep learning accelerator.

FIG. 4 illustrates selected details of an embodiment of a deep learningaccelerator.

FIG. 5 illustrates selected details of an embodiment of a processingelement of a deep learning accelerator.

FIG. 6 illustrates selected details of an embodiment of a router of aprocessing element.

FIG. 7A illustrates selected details of an embodiment of processingassociated with a router of a processing element.

FIG. 7B illustrates selected details of an embodiment of generating andproviding backpressure information associated with a compute element ofa processing element.

FIG. 7C illustrates selected details of an embodiment of generating andproviding backpressure information associated with a router of aprocessing element.

FIG. 7D illustrates selected details of an embodiment of stallingprocessing associated with a compute element of a processing element.

FIG. 8 illustrates selected details of an embodiment of a computeelement of a processing element.

FIG. 9A illustrates selected details of an embodiment of processing awavelet for task initiation.

FIG. 9B illustrates selected details of an embodiment of taskactivating.

FIG. 9C illustrates selected details of an embodiment of blockinstruction and unblock instruction execution.

FIGS. 10A and 10B illustrate selected details of high-level dataflowoccurring in an embodiment mapping multiple instances of a single neuronto respective sets of processing elements.

FIG. 11 illustrates an embodiment of tasks as used in a forward passstate machine, including dependency management via closeouts.

FIG. 12 illustrates selected details of an embodiment of flow associatedwith activation accumulation and closeout, followed by partial sumcomputation and closeout.

FIG. 13A illustrates selected details of an embodiment of a sparsewavelet.

FIG. 13B illustrates selected details of an embodiment of a densewavelet.

FIG. 14 illustrates selected details of an embodiment of creating andtransmitting a wavelet.

FIG. 15 illustrates selected details of an embodiment of receiving awavelet.

FIG. 16 illustrates selected details of an embodiment of consuming awavelet.

FIG. 17 illustrates selected details of an embodiment of a neuralnetwork.

FIG. 18A illustrates selected details of a first embodiment of anallocation of processing elements to neurons.

FIG. 18B illustrates selected details of a second embodiment of anallocation of processing elements to neurons.

FIG. 19 illustrates selected details of an embodiment of smearing aneuron across a plurality of processing elements.

FIG. 20 illustrates selected details of an embodiment of communicationbetween portions of split neurons.

FIG. 21A illustrates selected details of an embodiment of a Fabric InputData Structure Descriptor.

FIG. 21B illustrates selected details of an embodiment of a FabricOutput Data Structure Descriptor.

FIG. 21C illustrates selected details of an embodiment of a 1D MemoryVector Data Structure Descriptor.

FIG. 21D illustrates selected details of an embodiment of a 4D MemoryVector Data Structure Descriptor.

FIG. 21E illustrates selected details of an embodiment of a CircularMemory Buffer Data Structure Descriptor.

FIG. 22A illustrates selected details of an embodiment of a CircularMemory Buffer Extended Data Structure Descriptor.

FIG. 22B illustrates selected details of an embodiment of a 4D MemoryVector Extended Data Structure Descriptor.

FIG. 23 illustrates selected details of accessing operands in accordancewith data structure descriptors.

FIG. 24 illustrates selected details of an embodiment of decoding a datastructure descriptor.

FIG. 25A illustrates selected details of an embodiment of a multipleoperand instruction.

FIG. 25B illustrates selected details of an embodiment of a one source,no destination operand instruction.

FIG. 25C illustrates selected details of an embodiment of an immediateinstruction.

FIG. 26 illustrates selected details of processing in accordance withmicrothreading.

FIG. 27A illustrates an embodiment of a pipeline flow for StochasticGradient Descent (SGD).

FIG. 27B illustrates an embodiment of a pipeline flow for Mini-BatchGradient Descent (MBGD).

FIG. 27C illustrates an embodiment of a pipeline flow for ContinuousPropagation Gradient Descent (CPGD).

FIG. 27D illustrates an embodiment of a pipeline flow for ContinuousPropagation Gradient Descent (CPGD) with Reverse CheckPoint (RCP).

FIGS. 28A-28E illustrate various aspects of forward pass and backwardpass embodiments in accordance with SGD, MBGD, CPGD, and RCP processing.

FIG. 29 illustrates selected details of an embodiment of a processorcomprising a floating-point unit and enabled to perform stochasticrounding.

FIG. 30A illustrates selected details of an embodiment of afloating-point instruction that optionally specifies stochasticrounding.

FIG. 30B illustrates selected details of an embodiment of afloating-point control register associated with controlling stochasticrounding, programmable exponent bias, and floating-point computationvariations.

FIG. 30C illustrates selected details of an embodiment of a mantissa ofa result of a floating-point operation, subject to normalization androunding.

FIG. 30D illustrates selected details of an embodiment of a normalizedmantissa of a result of a floating-point operation after normalization,and subject to rounding.

FIG. 30E illustrates selected details of an embodiment of afloating-point number datatype.

FIG. 31 illustrates a flow diagram of selected details of an embodimentof a processor executing a floating-point instruction with optionalstochastic rounding.

FIG. 32 illustrates a flow diagram of selected details of an embodimentof floating-point processing in accordance with a programmable exponentbias.

List of Reference Symbols in Drawings Ref Symbol Element Name  100Neural Network System  110 Combined Server(s)  111 LAN  112 100 Gb  113Placements  114 Weights  115 Weights  120 Deep Learning Accelerator  121FPGAs  122 PEs  123 Coupling  130 Autonomous Vehicle  131 CPUs  132 CRM 133 IEs  135 Camera  140 Cell Phone  141 CPUs  142 CRM  143 IEs  145Camera  150 Placement Server(s)  151 CPUs  152 CRM  160 ConnectionServer(s)  161 CPUs  162 CRM  164 NICs  180 Internet  200 Neural NetworkSoftware  210 Placement Server(s) SW  212 Neuron to PE Mapping SW  220Connection Server(s) SW  224 100 Gb NIC Driver  225 Training InfoProvider SW  226 Weight Receiver SW  230 Autonomous Vehicle SW  232Video Camera SW  233 Inference Engine(s) SW  234 Navigating SW  240 CellPhone SW  242 Still Camera SW  243 Inference Engine(s) SW  244 PostingSW  250 Misc SW on FPGAs  260 Task SW on PEs  300 Neural NetworkTraining/Inference, Overall  310 Place Neurons  320 Initialize FPGAs 330 Initialize PEs  340 Training Data => PEs  350 Forward Pass, DeltaPass, Chain Pass, Update Weights  360 Training Complete?  370 WeightsOut  380 Use Weights for Inference  400 Deep Learning Accelerator  401Forward  402 Delta  403 Chain  410 ASIC  411 ASIC  412 Wafer  420 I/OFPGAs  430 North coupling  431 East coupling  432 South coupling  433West coupling  497 Particular PE  498 Particular PE  499 PE  500 PE  510Router  511 West  512 Skip West  513 North  514 Skip East  515 East  516South  520 Compute Element  521 Off Ramp  522 On Ramp  600 Router  610Data In  611 skipX+  612 skipX−  613 X+  614 X−  615 Y+  616 Y−  617 OnRamp  620 Data Out  621 skipX+  622 skipX−  623 X+  624 X−  625 Y+  626Y−  627 Off Ramp  630 Stall Out  631 skipX+  632 skipX−  633 X+  634 X− 635 Y+  636 Y−  637 On Ramp  640 Stall In  641 skipX+  642 skipX−  643X+  644 X−  645 Y+  646 Y−  647 Off Ramp  650 Data Queues  651 Write Dec 652 Out  653 Sources  654 Router Sched  656 Gen Stall  657 Stall  660Control Info  661 Dest  662 Sent  670 Src  710 Wavelet Ingress  711 Waitfor Wavelet  712 Receive Wavelet  713 Wavelet => Router Q  740Generating and Providing Backpressure Information, Overall  741 CE of PE 742 Router of PE  743 Start  744 Determine Input Q(s) over Threshold 745 Determine Colors Associated with Input Q(s)  746 ProvideStall/Ready to Router  747 Provide Wavelet to CE in Accordance withStall/Ready  748 End  750 Generating and Providing BackpressureInformation, Overall  751 Router of PE  752 CE of PE  753 Router(s) ofNeighbor(s)  755 Start  756 Determine Data Queue(s) Over Threshold  757Check Color Sources  758 Determine Stall/Ready Colors for CE, Neighbors 759 Provide Stall/Ready to CE, Neighbors  760 Provide Wavelet to Routerin Accordance with Stall/Ready  761 Provide Wavelet to Router inAccordance with Stall/Ready  762 End  780 Stalling Processing, Overall 781 CE of PE  782 Start  783 Determine Full Output Q(s)  784 DetermineColors Associated Output Q(s)  785 Stall Processing for ColorsAssociated with Full Output Q(s)  786 End  800 CE  812 Terminate  820Off Ramp  822 Hash  824 Qdistr  830 Picker  834 PC  836 I-Seq  837 OnRamp  840 Dec  842 RF  844 D-Seq  845 UT State  846 DSRs  847 Off Ramp 848 D-Store  852 Data Path  854 Memory  859 Output Queues  859.0 OutputQ0  859.N Output QN  860 On Ramp  890 Base  896 Scheduling Info  897Input Qs  897.0 Input Q0  897.N Input QN  898 Active Bits  898.0 ActiveBit 0  898.N Active Bit N  899 Block Bits  899.0 Block Bit 0  899.NBlock Bit N  900 Processing a Wavelet for Task Initiation, Overall  901Start  902 Select Ready Wavelet for Task Initiation  903 Control/Data? 904 Add (Color * 4) to Base Register to Form Instruction Address  905Fetch Instructions From Memory at Instruction Address  906 ExecuteFetched Instruction(s)  908 Not Terminate  909 Terminate  910 Add LowerIndex Bits to Base Register to Form Instruction Address  919 End  920Task Activating, Overall  921 Start  923 Activate Operation for Color(s) 924 Activate Color(s)  925 Picker Selects Color  926 Initiate Task,Deactivate Color  929 End  940 Block and Unblock Instruction ProcessingFlow, Overall  941 Start  942 Fetch, Decode Instruction  943 BlockInstruction?  944 Block Color(s)  945 Unblock Instruction?  946 UnblockColor(s)  947 Execute Instruction  949 End 1040 Neural Network Portion1041 (Neuron) A 1042 (Neuron) B 1043 (Neuron) C 1044 (Neuron) D 1045(Neuron) E 1046 (Neuron) F 1060 Processing Element Array Portion 1061(Activation) aA 1062 (Activation) aB 1063 (Activation) aC 1064(Activation) aD 1065 (Activation) aE 1066 (Activation) aF 1070 PE0 1071PE1 1072 PE2 1073 PE3 1074 PE4 1075 PE5 1076 PE6 1077 PE7 1078 PE8 1080(weight) wAD 1081 (weight) wAE 1082 (weight) wAF 1083 (weight) wBD 1084(weight) wBE 1085 (weight) wBF 1086 (weight) wCD 1087 (weight) wCE 1088(weight) wCF 1090 PSA 1091 PSA 1092 PSA 1101 f_rxact:acc 1102f_rxact:close 1103 f_psum:prop 1104 f_txact:tx 1111 Activations fromPrior Layer 1112 Closeouts from Prior Layer 1113 Flow 1114 Wake 1115Reschedule 1116 Start Psums 1121 Activations to Next Layer 1122Closeouts to Next Layer 1130 Prop Psums 1131 Prop Psums 1200 ActivationAccumulation/Closeout and Partial Sum Computation/Closeout, Overall 1201Start 1202 Receive Activation 1203 Accumulate Activations 1204 ReceiveActivation Closeout 1205 Start Partial Sum Ring 1206 Receive Partial Sum1207 Compute Partial Sum 1208 Transmit Partial Sum 1209 TransmitActivations 1210 Transmit Closeout 1211 End 1301 Sparse Wavelet 1302Sparse Wavelet Payload 1320 Control Bit 1321 Index 1321.1 Lower IndexBits 1321.2 Upper Index Bits 1322 Sparse Data 1324 Color 1331 DenseWavelet 1332 Dense Wavelet Payload 1340 Control Bit 1343.1 Dense Data1343.2 Dense Data 1344 Color 1400 Wavelet Creation Flow, Overall 1401Start 1402 Initialize PEs 1403 Set Source 1404 Set Destination (Fabric)DSR 1405 Fetch/Decode Instruction with Destination DSR 1406 Read DSR(s)1407 Read (Next) Source Data Element(s) from Queue/Memory 1408 ProvideData Element(s) as Wavelet to Output Queue 1409 More Data Elements? 1411Transmit Wavelet(s) to Fabric 1412 Receive Wavelet(s) from Fabric 1410End 1420 CE of Transmitting PE 1430 Router of Transmitting PE 1440Router of Receiving PE 1500 Wavelet Receive Flow, Overall 1501 Start1502 Initialize PEs 1503 Receive Wavelet at Router 1504 To Other PE(s)?1505 Transmit Wavelet to Output(s) 1506 For Local CE? 1507 Write Waveletto Picker Queue 1510 End 1520 Router of Receiving PE 1530 CE ofReceiving PE 1600 Wavelet Consumption Flow, Overall 1601 Start 1602Picker Selects Wavelet for Processing 1603 Fetch, Execute Instructions1604 End 1700 Neural Network 1710 Input Layer 1711 N11 1712 N12 1713 N131720 Internal Layers 1721 N21 1721.1, 1721.2 1/2 N21 portions,respectively 1722 N22 1722.1, 1722.2 1/2 N22 portions, respectively 1723N23 1723.1, 1723.2 1/2 N23 portions, respectively 1724 N24 1724.1,1724.2 1/2 N24 portions, respectively 1731 N31 1731.1, 1731.2, 1/4 N31portions, respectively 1731.3, 1731.4 1732 N32 1732.1, 1732.2, 1/4 N32portions, respectively 1732.3, 1732.4 1733 N33 1740 Output Layer 1741N41 1742 N42 1791 communication 1791.1 communication portion 1792communication 1792.1 communication portion 1793 communication 1793.1communication portion 1820 PE0 1821 PE1 1822 PE2 1823 PE3 1824 PE4 1825PE5 1910 in0 1911 in1 1912 in2 1913 in3 1914 in4 1915 in5 1920 out0 1921out1 1922 out2 1923 out3 1924 out4 1925 out5 1930.1 1/2 Local Compute1930.2 1/2 Local Compute 1940.1 1/2 Local Storage 1940.2 1/2 LocalStorage 1950.1 Additional Compute 1950.2 Additional Compute 1960.1Additional Storage 1960.2 Additional Storage 1970 AdditionalCommunication 2000 Wafer Portion 2040, 2041, 2043, 2044 coupling betweenadjacent PEs, respectively 2050, 2051, 2052, 2053, portion of couplingbetween adjacent PEs, respectively 2054, 2055, 2056, 2057 2060communication 2100 Fabric Input Data Structure Descriptor 2101 Length2102 UTID (Microthread Identifier) 2103 UE (Microthread Enable) 2104 SW(SIMD Width) 2105 AC (Activate Color) 2106 Term (Terminate Microthreadon Control Wavelet) 2107 CX (Control Wavelet Transform Enable) 2108 US(Microthread Sparse Mode) 2109 Type 2110 SS (Single Step) 2111 SA (SaveAddress/Conditional Single Step Mode) 2112 SC (Color Specified, NormalMode) 2113 SQ (Queue Specified, Normal Mode) 2114 CH (Color, High Bits)2120 Fabric Output Data Structure Descriptor 2121 Length 2122 UTID(Microthread Identifier) 2123 UE (Microthread Enable) 2124 SW (SIMDWidth) 2125 AC (Activate Color) 2126 Color 2127 C (Output Control Bit)2128.1 Index Low 2128.2 Index High 2129 Type 2130 SS (Single Step) 2131SA (Save Address/Conditional Single Step Mode) 2132 WLI (Wavelet IndexSelect) 2140 1D Memory Data Structure Descriptor 2141 Length 2142 BaseAddress 2149 Type 2150 SS (Single Step) 2151 SA (SaveAddress/Conditional Single Step Mode) 2152 WLI (Wavelet Index Select)2153 Stride 2160 4D Memory Data Structure Descriptor 2161 Length 2161.1Length Lower Bits 2161.2 Length Upper Bits 2162 Base Address 2169 Type2170 SS (Single Step) 2171 SA (Save Address/Conditional Single StepMode) 2172 WLI (Wavelet Index Select) 2180 Circular Memory Buffer DataStructure Descriptor 2181 Length 2182 Base Address 2184 SW (SIMD Width)2188 FW (FIFO Wrap Bit) 2189 Type 2190 SS (Single Step) 2191 SA (SaveAddress/Conditional Single Step Mode) 2192 WLI (Wavelet Index Select)2210 Circular Memory Buffer Extended Data Structure Descriptor 2211 Type2212 Start Address 2213 End Address 2214 FIFO 2215 Push (Activate) Color2216 Pop (Activate) Color 2240 4D Memory Vector Extended Data StructureDescriptor 2241 Type 2242 Dimensions 2243 DF (Dimension Format) 2244.1Stride Select (for Dimension) 1 2244.2 Stride Select (for Dimension) 22244.3 Stride Select (for Dimension) 3 2244.4 Stride Select (forDimension) 4 2245 Stride 2300 Data Structure Descriptor Flow, Overall2301 Start 2302 Set DSR(s) 2303 Fetch/Decode Instruction with DSR(s)2304 Read DSR(s) 2305 (optional) Set XDSR(s) 2306 (optional) ReadXDSR(s) 2310 Read (Next) Source Data Element(s) from Queue/Memory 2310ARead (Next) Source Data Element(s) from Queue/Memory 2311 Perform (Next)Operation(s) on Data Element(s) 2312 Write (Next) Destination DataElement(s) to Queue/Memory 2313 More Data Element(s)? 2316 End 2400 DataStructure Descriptor Decode Flow, Overall 2401 Start 2410 Fabric Vector2411 Type = Fabric? 2412 Access via DSD 2420 Memory Vector 2421 Type =XDSR? 2422 Read XDSR Specified via DSD 2423 Type = 4D Vector? 2424(optional) Read Stride Register(s) 2427 Access 1D via DSD 2428 Access 4Dvia XDSD 2429 Access Circular Buffer via XDSD 2499 End 2510 MultipleOperand Instruction 2511 Instruction Type 2512 Opcode 2513 Operand 0Encoding 2513.1 Operand 0 Type 2513.2 Operand 0 2514 Operand 1 Encoding2514.1 Operand 1 Type 2514.2 Operand 1 2515 Terminate 2520 One Source,No Destination Operand Instruction 2521 Instruction Type 2522 Opcode2523 Operand 1 Encoding 2523.1 Operand 1 Type 2523.2 Operand 1 2524Immediate 2525 Terminate 2530 Immediate Instruction 2531 InstructionType 2532 Opcode 2533.2 Operand 0 2534.1 Immediate Low 2534.2 ImmediateHigh 2534 Immediate 2600 Microthreaded Instruction Flow, Overall 2603Stall? 2605 Stall Resolved? 2606 Micro threading Enabled? 2607 SaveMicrothreaded Instruction Information 2608 Execute Next Instruction(s)2609 Stall Resolved? 2610 Read (Next) Source Data Element(s) fromQueue/Memory 2711 First Forward Pass 2712 Second Forward Pass 2721 FirstBackward Pass 2722 Second Backward Pass 2731 Mini-Batch Size (N) 2732Overhead 2733 Update Interval (U) 2751 Forward Pass 2761 Backward Pass2765 Forward Pass 2766 Backward Pass 2767 Weight Update Use 2771 ForwardPass 2781 Backward Pass 2785 Activation Storage 2786 RecomputedActivation Storage 2801 Previous Layer 2802 Subsequent Layer 2803Previous Layer 2804 Subsequent Layer 2810 Compute 2811 F 2812 B 2815Storage 2816 A 2817 W 2818 W 2820 Compute 2821 F 2822 B 2825 Storage2826 A 2827 W 2828 W 2829 A 2830 Compute 2835 Storage 2840 Compute 2845Storage 2881 A_(1,t) 2882 A_(2,t) 2883 A_(3,t) 2884 A′_(2,t) 2891Δ_(1,t) 2892 Δ_(2,t) 2893 Δ_(3,t) 2894 Δ′_(1,t) 2895 Δ′_(2,t) 2896Δ′_(3,t) 2900 Processor 2901 Floating-Point Unit (FPU) 2911 Multiplier2912 Accumulator 2913 Normalizer 2914 Incrementer 2915 Exponent DP (DataPath) 2920 Instruction Decode Logic 2921 Random Number Generators (RNGs)2922 N-bit Adder 2925 FP Control Register 2925.1 Static Rounding ModeBits 2925.2 Static RNG Bits 2925.3 FTZ (Flush To Zero) 2925.4 Max BiasedExponent Normal 2925.5 Zero Biased Exponent Normal 2925.6 Exponent Bias2925.7 Large Exponent 2950 Instruction 2951 Src A 2952 Src B 2953Intermediate Result 2954 Src C 2955 Mantissa 2955.1 Leading Zeros 2955.2Other Bits 2956 Normalized Mantissa 2957.1 N Most Significant Lower Bits2958 Mantissa Bits Subject to Rounding 2961 RNG Selector 2962 N-bitRandom Number 2963 Carry Bit 2964 Stochastically Rounded Mantissa 2965Stochastically Rounded Biased Exponent 2970 Exponent Bias 3002.1 Unit ofLeast Precision (ULP) 3003 Lower Bits 3003.2 Least Significant LowerBits 3021 Rounding Mode Bits 3022 RNG Bits 3023 OpCode Bits 3024 SourceBits 3025 Dest Bits 3050 FP Number 3051 Sign 3052 Biased Exponent 3053Mantissa 3100 Start 3110 Decode FP Multiply-Accumulate Instruction 3120Perform FP Multiply-Accumulate Operation 3130 Normalize Result 3140Stochastic Rounding? 3141 No 3142 Yes 3150 Deterministically RoundMantissa of Result 3160 Select N-bit Random Number 3170 Add N-bit RandomNumber and N Most Significant Lower Bits 3180 Carry? 3181 No 3182 Yes3190 Increment ULP 3198 Provide Rounded Result 3199 End 3200 Start 3201Program Exponent Bias 3202 Perform Computation(s) 3203 Change ExponentBias? 3204 No 3205 Yes

DETAILED DESCRIPTION

A detailed description of one or more embodiments of the invention isprovided below along with accompanying figures illustrating selecteddetails of the invention. The invention is described in connection withthe embodiments. The embodiments herein are understood to be merelyexemplary, the invention is expressly not limited to or by any or all ofthe embodiments herein, and the invention encompasses numerousalternatives, modifications, and equivalents. To avoid monotony in theexposition, a variety of word labels (such as: first, last, certain,various, further, other, particular, select, some, and notable) may beapplied to separate sets of embodiments; as used herein such labels areexpressly not meant to convey quality, or any form of preference orprejudice, but merely to conveniently distinguish among the separatesets. The order of some operations of disclosed processes is alterablewithin the scope of the invention. Wherever multiple embodiments serveto describe variations in process, system, and/or program instructionfeatures, other embodiments are contemplated that in accordance with apredetermined or a dynamically determined criterion perform staticand/or dynamic selection of one of a plurality of modes of operationcorresponding respectively to a plurality of the multiple embodiments.Numerous specific details are set forth in the following description toprovide a thorough understanding of the invention. The details areprovided for the purpose of example and the invention may be practicedaccording to the claims without some or all of the details. For thepurpose of clarity, technical material that is known in the technicalfields related to the invention has not been described in detail so thatthe invention is not unnecessarily obscured.

introduction

This introduction is included only to facilitate the more rapidunderstanding of the Detailed Description; the invention is not limitedto the concepts presented in the introduction (including explicitexamples, if any), as the paragraphs of any introduction are necessarilyan abridged view of the entire subject and are not meant to be anexhaustive or restrictive description. For example, the introductionthat follows provides overview information limited by space andorganization to only certain embodiments. There are many otherembodiments, including those to which claims will ultimately be drawn,discussed throughout the balance of the specification.

In an aspect conceptually related to numerical representation for neuralnetworks, techniques in advanced deep learning provide improvements inone or more of accuracy, performance, and energy efficiency. An array ofprocessing elements comprising a portion of a neural network acceleratorperforms flow-based computations on wavelets of data. Each processingelement has a respective compute element and a respective routingelement. Each compute element has a respective floating-point unitenabled to optionally and/or selectively perform floating-pointoperations in accordance with a programmable exponent bias and/orvarious floating-point computation variations. An example floating-pointcomputation variation is operating in accordance with customfloating-point number formats comprising a biased exponent field havingmore bits in conjunction with a mantissa field having correspondinglyfewer bits. Another example floating-point computation variation isusing the maximum biased exponent (e.g. the biased exponent field is allones) for IEEE 754 compatibility (e.g. NaN and infinity representation)or alternatively using the maximum biased exponent to representfloating-point values similar to floating-point values represented byother-than the maximum biased exponent. Another example floating-pointcomputation variation is a saturated rounding mode that rounds anyresult greater in magnitude than the maximum magnitude to the maximummagnitude (instead of to infinity), which is represented using themaximum biased exponent. Another example floating-point computationvariation is using the zero biased exponent (e.g. the biased exponentfield is all zeros) for IEEE 754 compatibility (e.g. subnormalrepresentation) or alternatively using the zero biased exponent torepresent floating-point values similar to floating-point valuesrepresented by other-than the zero biased exponent. Another examplefloating-point computation variation is a flush-to-zero mode thatflushes subnormal values to zero (instead of representing subnormalresults using the zero biased exponent). In some circumstances, theprogrammable exponent bias and/or the floating-point computationvariations enable neural network processing with improved accuracy,decreased training time, decreased inference latency, and/or increasedenergy efficiency.

In an aspect conceptually related to floating-point computations foraccelerated deep learning, techniques in advanced deep learning provideimprovements in one or more of accuracy, performance, and energyefficiency. An array of processing elements comprising a portion of aneural network accelerator performs flow-based computations on waveletsof data. Each processing element has a respective compute element and arespective routing element. Each compute element has a respectivefloating-point unit enabled to perform stochastic rounding, thus in somecircumstances enabling reducing systematic bias in long dependencychains of floating-point computations. The long dependency chains offloating-point computations are performed, e.g., to train a neuralnetwork or to perform inference with respect to a trained neuralnetwork.

In an aspect conceptually related to data structure descriptors foraccelerated deep learning, techniques in advanced deep learning provideimprovements in one or more of accuracy, performance, and energyefficiency. An array of processing elements performs flow-basedcomputations on wavelets of data. Each processing element has arespective compute element and a respective routing element. Eachcompute element has memory. Each router enables communication viawavelets with at least nearest neighbors in a 2D mesh. Routing iscontrolled by respective virtual channel specifiers in each wavelet androuting configuration information in each router. Instructions executedby the compute element include one or more operand specifiers, some ofwhich specify a data structure register storing a data structuredescriptor. The data structure descriptor describes an operand as afabric vector or a memory vector. The data structure descriptor furtherdescribes the memory vector as one of a one-dimensional vector, afour-dimensional vector, or a circular buffer vector. Optionally, thedata structure descriptor specifies an extended data structure registerstoring an extended data structure descriptor. The extended datastructure descriptor specifies parameters relating to a four-dimensionalvector or a circular buffer vector.

In an aspect conceptually related to fabric vectors for accelerated deeplearning, techniques in advanced deep learning provide improvements inone or more of accuracy, performance, and energy efficiency. An array ofprocessing elements performs flow-based computations on wavelets ofdata. Each processing element has a respective compute element and arespective routing element. Each compute element has memory. Each routerenables communication via wavelets with at least nearest neighbors in a2D mesh. Routing is controlled by respective virtual channel specifiersin each wavelet and routing configuration information in each router.Instructions executed by the compute element include one or more operandspecifiers, some of which specify a data structure register storing adata structure descriptor. The data structure descriptor describes anoperand as a fabric vector or a memory vector. The data structuredescriptor further describes the length of the fabric vector, whetherthe fabric vector is eligible for microthreading, and a number of dataelements of the fabric vector to receive, transmit, and/or process inparallel. The data structure descriptor further specifies virtualchannel and task identification information relating to processing thefabric vector, whether to terminate upon receiving a control wavelet,and whether to mark an outgoing wavelet as a control wavelet.

In an aspect conceptually related to neuron smearing for accelerateddeep learning, techniques in advanced deep learning provide improvementsin one or more of accuracy, performance, and energy efficiency. An arrayof processing elements performs flow-based computations on wavelets ofdata. Each processing element has a respective compute element and arespective routing element. Each compute element has memory. Each routerenables communication via wavelets with at least nearest neighbors in a2D mesh. Routing is controlled by respective virtual channel specifiersin each wavelet and routing configuration information in each router. Atleast a first single neuron is implemented using resources of aplurality of the array of processing elements. At least a portion of asecond neuron is implemented using resources of one or more of theplurality of processing elements. In some usage scenarios, the foregoingneuron implementation enables greater performance by enabling a singleneuron to use the computational resources of multiple processingelements and/or computational load balancing across the processingelements while maintaining locality of incoming activations for theprocessing elements.

In an aspect conceptually related to microthreading for accelerated deeplearning, techniques in advanced deep learning provide improvements inone or more of accuracy, performance, and energy efficiency. An array ofprocessing comprising compute elements and routers performs flow-basedcomputations on wavelets of data. Some instructions are performed initerations, such as one iteration per element of a fabric vector orFIFO. When sources for an iteration of an instruction are unavailable,and/or there is insufficient space to store results of the iteration,indicators associated with operands of the instruction are checked todetermine when other work can be performed. In some scenarios, otherwork cannot be performed and processing stalls. In other scenarios,information about the instruction is saved, the other work is performed,and sometime after the sources become available and/or sufficient spaceto store the results becomes available, the iteration is performed usingthe saved information.

In an aspect conceptually related to task activating for accelerateddeep learning, techniques in advanced deep learning provide improvementsin one or more of accuracy, performance, and energy efficiency. An arrayof processing elements performs flow-based computations on wavelets ofdata. Each processing element has a respective compute element and arespective routing element. Each compute element has processingresources and memory resources. Each router enables communication viawavelets with at least nearest neighbors in a 2D mesh. Routing iscontrolled by respective virtual channel specifiers in each wavelet androuting configuration information in each router. The virtual channelspecifiers correspond to respective virtual channels. Execution of anactivate instruction or completion of a fabric vector operationactivates one of the virtual channels. A particular virtual channel isselected from a pool comprising previously activated virtual channelsand virtual channels associated with previously received wavelets. Atask corresponding to the selected virtual channel is activated, e.g.,initiated, by executing instructions corresponding to the selectedvirtual channel.

In an aspect conceptually related to backpressure for accelerated deeplearning, techniques in advanced deep learning provide improvements inone or more of accuracy, performance, and energy efficiency. An array ofprocessing elements performs flow-based computations on wavelets ofdata. Each processing element comprises a respective compute element anda respective routing element. Each compute element comprises virtualinput queues. Each router enables communication via wavelets with atleast nearest neighbors in a 2D mesh. Routing is controlled byrespective virtual channel specifiers in each wavelet and routingconfiguration information in each router. Each router comprises dataqueues. The virtual input queues of the compute element and the dataqueues of the router are managed in accordance with the virtualchannels. Backpressure information, per each of the virtual channels, isgenerated, communicated, and used to prevent overrun of the virtualinput queues and the data queues.

In an aspect conceptually related to task synchronization foraccelerated deep learning, techniques in advanced deep learning provideimprovements in one or more of accuracy, performance, and energyefficiency. An array of processing elements performs flow-basedcomputations on wavelets of data. Each processing element has arespective compute element and a respective routing element. Eachcompute element has memory. Each router enables communication viawavelets with at least nearest neighbors in a 2D mesh. Routing iscontrolled by respective virtual channel specifiers in each wavelet androuting configuration information in each router. A particular one ofthe compute elements conditionally selects for task initiation apreviously received wavelet specifying a particular one of the virtualchannels. The conditional selecting excludes the previously receivedwavelet for selection until at least block/unblock state maintained forthe particular virtual channel is in an unblock state. The computeelements execute block/unblock instructions to modify the block/unblockstate.

In an aspect conceptually related to dataflow triggered tasks foraccelerated deep learning, techniques in advanced deep learning provideimprovements in one or more of accuracy, performance, and energyefficiency. An array of processing elements performs flow-basedcomputations on wavelets of data. Each processing element has arespective compute element and a respective routing element. Eachcompute element has memory. Each router enables communication viawavelets with at least nearest neighbors in a 2D mesh. Routing iscontrolled by respective virtual channel specifiers in each wavelet androuting configuration information in each router. A particular one ofthe compute elements receives a particular wavelet comprising aparticular virtual channel specifier and a particular data element.Instructions are read from the memory of the particular compute elementbased at least in part on the particular virtual channel specifier. Theparticular data element is used as an input operand to execute at leastone of the instructions.

In an aspect conceptually related to control wavelets for accelerateddeep learning, techniques in advanced deep learning provide improvementsin one or more of accuracy, performance, and energy efficiency. An arrayof processing elements performs flow-based computations on wavelets ofdata. Each processing element has a respective compute element and arespective routing element. Each compute element has a memory. Eachrouter enables communication via wavelets with at least nearestneighbors in a 2D mesh. A particular one of the compute elementsreceives a wavelet. If a control specifier of the wavelet is a firstvalue, then instructions are read from the memory of the particularcompute element in accordance with an index specifier of the wavelet. Ifthe control specifier is a second value, then instructions are read fromthe memory of the particular compute element in accordance with avirtual channel specifier of the wavelet. Then the particular computeelement initiates execution of the instructions.

In an aspect conceptually related to wavelet representation foraccelerated deep learning, techniques in advanced deep learning provideimprovements in one or more of accuracy, performance, and energyefficiency. An array of processing elements performs flow-basedcomputations on wavelets of data. Each processing element has arespective compute element and a respective routing element. Eachcompute element has dedicated storage. Each router enables communicationwith at least nearest neighbors in a 2D mesh. The communication is viawavelets in accordance with a representation comprising an indexspecifier, a virtual channel specifier, an index specifier, a dataelement specifier, and an optional control/data specifier. The virtualchannel specifier and the index specifier are associated with one ormore instructions. The index specifier and the data element areoptionally associated with operands of the one or more instructions.

In an aspect conceptually related to continuous propagation foraccelerated deep learning, techniques in advanced deep learning provideimprovements in one or more of accuracy, performance, and energyefficiency, such as accuracy of learning, accuracy of prediction, speedof learning, performance of learning, and energy efficiency of learning.An array of processing elements performs flow-based computations onwavelets of data. Each processing element has a respective computeelement and a respective routing element. Each compute element hasprocessing resources and memory resources. Each router enablescommunication via wavelets with at least nearest neighbors in a 2D mesh.Stochastic gradient descent, mini-batch gradient descent, and continuouspropagation gradient descent are techniques usable to train weights of aneural network modeled by the processing elements. Reverse checkpoint isusable to reduce memory usage during the training.

A first example of accelerated deep learning is using a deep learningaccelerator to train a neural network. A second example of accelerateddeep learning is using a deep learning accelerator to operate a trainedneural network to perform inferences. A third example of accelerateddeep learning is using a deep learning accelerator to train a neuralnetwork and subsequently perform inference with any one or more of thetrained neural network, information from same, and a variant of same.

Examples of neural networks include Fully Connected Neural Networks(FCNNs), Recurrent Neural Networks (RNNs), Convolutional Neural Networks(CNNs), Long Short-Term Memory (LSTM) networks, autoencoders, deepbelief networks, and generative adversarial networks.

An example of training a neural network is determining one or moreweights associated with the neural network, such as by hardwareacceleration via a deep learning accelerator. An example of making aninference is using a trained neural network to compute results byprocessing input data based on weights associated with the trainedneural network. As used herein, the term ‘weight’ is an example of a‘parameter’ as used in various forms of neural network processing. Forexample, some neural network learning is directed to determiningparameters that are then usable for performing neural network inferencesusing the parameters.

For example, the parameters are variously any combination of scalars,vectors, matrices, tensors, and so forth, such as arrangements of anarbitrary number and an arbitrary complexity of elements. For example,the parameters are of various dimensions, such as one-dimensional,two-dimensional, three-dimensional, and otherwise multidimensional. Forexample, the parameters are of various datatypes, such as, integer andfloating point. For example, the parameters (or respective portionsthereof, e.g., an exponent or a mantissa) are represented with variousprecisions (sometimes referred to as widths), such as, 8-bit, 16-bit,32-bit, 64-bit, and so forth.

A neural network processes data according to a dataflow graph comprisinglayers of neurons. Stimuli (e.g., input data) are received by an inputlayer of neurons and the computed results of the dataflow graph (e.g.,output data) are provided by an output layer of neurons. Example layersof neurons include input layers, output layers, rectified linear unitlayers, fully connected layers, recurrent layers, long short-term memorylayers, convolutional layers, kernel layers, dropout layers, and poolinglayers. A neural network is conditionally and/or selectively trained,subject to hardware acceleration. After being trained, a neural networkis conditionally and/or selectively used for inference, subject tohardware acceleration.

An example of a deep learning accelerator is one or more relativelyspecialized hardware elements operating in conjunction with one or moresoftware elements to train a neural network and/or perform inferencewith a neural network relatively more efficiently than using relativelyless specialized hardware elements. Some implementations of therelatively specialized hardware elements include one or more hardwarelogic circuitry elements such as transistors, resistors, inductors,capacitors, wire interconnects, combinatorial logic (e.g., NAND, NOR)gates, latches, register files, memory arrays, tags for memory arrays,content-addressable memories, flash, ROM, DRAM, SRAM,Serializer/Deserializer (SerDes), I/O drivers, and the like, such asimplemented via custom logic, synthesized logic, ASICs, and/or FPGAs.Some of the relatively less specialized hardware elements includeconventional CPUs and conventional GPUs.

An example implementation of a deep learning accelerator is enabled toprocess dataflow in accordance with computations performed for trainingof a neural network and/or inference with a neural network. Some deeplearning accelerators comprise processing elements coupled via a fabricand enabled to communicate with each other via the fabric. Sometimes theprocessing elements and the fabric are collectively referred to as afabric of processing elements.

An example implementation of a processing element is enabled tocommunicate and process wavelets. In various circumstances, the waveletscorrespond to dataflow and/or instruction flow in accordance withcommunication and/or processing enabling computations performed fortraining of and/or inference using a neural network.

An example processing element comprises a router to communicate waveletsvia the fabric and a compute element to process the wavelets. An examplerouter is coupled to a plurality of elements: a fabric, an off ramp tothe compute element, and an on ramp from the compute element. An examplecoupling between the router and the fabric enables communication betweenthe router and, e.g., four logically and/or physically adjacentprocessing elements. The router variously receives wavelets from thefabric and the on ramp. The router variously transmits wavelets to thefabric and the off ramp.

An example implementation of a compute element is enabled to processwavelets by initiating tasks and executing instructions associated withthe wavelets, and accessing data associated with the wavelets and/or theinstructions. The instructions are in accordance with an instruction setarchitecture comprising arithmetic instructions, control flowinstructions, datatype conversion instructions, configurationinstructions, fabric management instructions, and load/storeinstructions. The instructions operate on operands comprising variousdatatypes, e.g., integer datatypes and floating-point datatypes ofvarious widths. The operands variously comprise scalar operands andvector operands. In various embodiments and/or usage scenarios, a vectorvariously represents, e.g., weights of a neural network, inputs orstimuli of a neural network, activations of a neural network, and/orpartial sums of a neural network. In some scenarios, a vector is asparse vector (e.g., a vector of neuron activations) and comprisessparse data elements (e.g., only non-zero elements). In some otherscenarios, a vector is a dense vector (e.g., pixel values) and comprisesdense data elements (e.g., all elements of the vector, including zeroelements).

An example compute element comprises hardware elements that collectivelyexecute the instructions associated with a wavelet by performingoperations specified by the instructions (e.g., arithmetic operations,control flow operations, and load/store operations). Examples of thehardware elements include picker queues, a picker, a task definitiontable, an instruction sequencer, an instruction decoder, a datasequencer, a register file, a memory, a pseudo-random number generator,and an ALU. Some implementations of the hardware elements are inaccordance with hardware logic circuitry elements as described elsewhereherein. Sometimes a compute element is referred to as a compute engine.Sometimes the compute scheduler is referred to as a picker and thecompute scheduler queues are referred to as picker queues.

An example fabric is a collection of logical and/or physical couplingsbetween processing elements and/or within a single processing element.The fabric is usable to implement logical and/or physical communicationtopologies such as a mesh, a 2D mesh, a 3D mesh, a hypercube, a torus, aring, a tree, or any combination thereof Δn example of a physicalcoupling between processing elements is a set of physical interconnects(comprising optional and/or selective buffering) betweenphysically-coupled processing elements. A first example ofphysically-coupled processing elements is immediately physicallyadjacent processing elements, such as a first processing element locateddirectly beside (such as ‘north’, ‘south’, ‘east’, or ‘west’) of asecond processing element. A second example of physically-coupledprocessing elements is relatively physically nearby processing elements,such as a first processing element located within a relatively smallnumber of intervening processing elements, e.g., one or two ‘rows’and/or ‘columns’ away from a second processing element. A third exampleof physically-coupled processing elements is relatively physically faraway processing elements, such as a first processing element locatedphysical relatively far away from a second processing element, such as adistance limited by signal propagation (with or without optional and/orselective buffering) within a clock cycle and/or clock sub-cycleassociated with the processing elements. An example of physical couplingwithin a single processing element (having, e.g., a compute element anda router) is an on ramp coupling output information from the computeelement to the router, and an off ramp coupling input information fromthe router to the compute element. In some situations, the router routesinformation from the on ramp to the off ramp.

An example of a logical coupling between processing elements is avirtual channel as implemented by routers within processing elements. Aroute between a first processing element and a second processing elementis implemented, e.g., by routers within processing elements along theroute forwarding in accordance with the virtual channel and routingconfiguration information. An example of a logical coupling within asingle particular processing element (having, e.g., a router) is avirtual channel as implemented by the router, enabling the particularprocessing element to send information via the virtual channel to theparticular processing element. The router forwards “internally” withrespect to the particular processing element in accordance with thevirtual channel and routing configuration information.

An example wavelet is a bundle of information communicated betweenprocessing elements via the fabric. An example wavelet comprises awavelet payload and a color. A wavelet payload comprises data and isassociated with instructions. A first response to a wavelet received bya compute element of a processing element comprises the compute elementinitiating a task, such as corresponding to processing of instructionsassociated with the wavelet. A second response to a wavelet received bya compute element of a processing element comprises the compute elementprocessing data of the wavelet. Example types of wavelets include densewavelets and sparse wavelets, as well as data wavelets and controlwavelets.

Wavelets are used, for example, for communicating between processingelements. In a first scenario, a first processing element transmitswavelets to a second processing element. In a second scenario, anexternal device (e.g., an FPGA) transmits wavelets to a processingelement. In a third scenario, a processing element transmits wavelets toan external device (e.g., an FPGA).

An example virtual channel is one or more communication pathwaysspecified by a color and enabled, e.g., by a fabric and one or morerouters. A wavelet comprising a particular color is sometimes referredto as being associated with a particular virtual channel associated withthe particular color. A first example of a color is a fabric colorspecifying a virtual channel between two different processing elements.In some embodiments, a fabric color is a 5-bit integer. A second exampleof a color is a local color specifying a virtual channel from aprocessing element to the processing element. In some embodiments, acolor is a 6-bit integer and specifies one of a fabric color and a localcolor.

An example task comprises a collection of instructions executed inresponse to a wavelet. An example instruction comprises an operation andoptionally one or more operands specifying locations of data elements tobe processed in accordance with the operation. A first example of anoperand specifies data elements in memory. A second example of anoperand specifies data elements communicated (e.g., received ortransmitted) via the fabric. An example of a data sequencer determinesthe locations of data elements. An example of an instruction sequencerdetermines an address in memory of instructions associated with awavelet.

An example picker queue is enabled to hold wavelets received via an offramp of the fabric for processing in the compute element. An example ofa picker selects a wavelet from the picker queue for processing, and/orselects an active unblocked color for processing to initiate acorresponding task.

An example of storage is one or more elements enabled to retain stateinformation, e.g., any one or more of: a flip-flop, a latch or an arrayof latches, a register or an array of registers, a register file, amemory, a memory array, a magnetic storage device, an optical storagedevice, SRAM, DRAM, flash, and ROM. In various embodiments storage isvolatile (e.g., SRAM or DRAM) and/or non-volatile (e.g., flash or ROM).

An example of an Integrated Circuit (IC) is a collection of circuitryimplemented on one or more portions of semiconductor material, such as asingle die or a plurality of dice. An example of an Application-SpecificIntegrated Circuit (ASIC) is an IC designed for a particular use. Anexample of wafer-scale integration is implementing a system using all ora significant portion of a wafer as an element of the system, e.g., byleaving the wafer whole or substantially whole.

In some embodiments and/or usage scenarios, wafer-scale integrationenables connecting multiple elements in a system via wafer interconnectformed using silicon fabrication processes instead of via inter-chipinterconnect, and thus improves any one or more of improved performance,cost, reliability, and energy efficiency. As a specific example, asystem implemented using wafer-scale integration technology enablesimplementation of three million PEs on a single wafer, each of the PEshaving bandwidth to nearest physical neighbors that is greater than acomparable system using other-than wafer-scale integration technology.The greater bandwidth enables the system implemented using wafer-scaleintegration technology to relatively efficiently train and/or performinferences for larger neural networks than the system implemented usingother-than wafer-scale integration technology.

Acronyms

At least some of the various shorthand abbreviations (e.g., acronyms)defined here refer to certain elements used herein.

Acronym Description ASIC Application Specific Integrated Circuit CECompute Element CNN Convolutional Neural Network CPGD ContinuousPropagation Gradient Descent CPU Central Processing Unit CRM ComputerReadable Media DRAM Dynamic Random Access Memory DSD Data StructureDescriptor DSP Digital Signal Processor DSR Data Structure Register FCNNFully Connected Neural Network FP Floating-Point FPGA Field-ProgrammableGate Array FPU Floating-Point Unit FTZ Flush To Zero GPU GraphicsProcessing Unit HPC High-Performance Computing HW HardWare IC IntegratedCircuit IE Inference Engine LFSR Linear Feedback Shift Register LSBLeast Significant Bit LSTM Long Short-Term Memory MBGD Mini-BatchGradient Descent ML Machine Learning MSB Most Significant Bit PEProcessing Element PRN Pseudo Random Number PRNG Pseudo Random NumberGenerator RNG Random Number Generator RNN Recurrent Neural Network RCPReverse CheckPoint SGD Stochastic Gradient Descent SIMD SingleInstruction Multiple Data SRAM Static Random Access Memory SW SoftWareULP Unit of Least Precision XDSD eXtended Data Structure Descriptor XDSReXtended Data Structure Register

Example Embodiments

In concluding the introduction to the detailed description, what followsis a collection of example embodiments, including at least someexplicitly enumerated as “ECs” (Example Combinations), providingadditional description of a variety of embodiment types in accordancewith the concepts described herein; these examples are not meant to bemutually exclusive, exhaustive, or restrictive; and the invention is notlimited to these example embodiments but rather encompasses all possiblemodifications and variations within the scope of the issued claims andtheir equivalents.

EC1) A system comprising:

-   -   means for retaining a programmable exponent bias;    -   means for performing a floating-point operation in accordance        with the programmable exponent bias; and    -   wherein the means for performing is in accordance with        interpreting one or more floating-point operands of the        floating-point operation according to the programmable exponent        bias and further in accordance with producing one or more        floating-point results according to the programmable exponent        bias.

EC2) The system of EC1, wherein the means for performing comprises meansfor accessing at least one of the floating-point operands, and the atleast one of the floating-point operands comprises a biased exponentbiased in accordance with the programmable exponent bias.

EC3) The system of EC1, wherein at least one of the floating-pointresults comprises a biased exponent biased in accordance with theprogrammable exponent bias.

EC4) The system of EC2 or EC3, wherein the number of bits of the biasedexponent is independent of the programmable exponent bias.

EC5) The system of EC1, further comprising means for executing aprogrammed instruction to set the programmable exponent bias to aparticular value.

EC6) The system of EC5, wherein the programmed instruction is a firstprogrammed instruction, the means for performing is responsive toexecuting a second programmed instruction, and the system furthercomprises a programmable processor comprising the means for performing.

EC7) The system of EC1, wherein the means for performing is operablewith the programmable exponent bias being a first value during a firstperiod of time and with the programmable exponent bias being a secondvalue during a second period of time, and the first value is distinctfrom the second value.

EC8) The system of EC7, further comprising means for executing aprogrammed instruction to set the programmable exponent bias from thefirst value to the second value.

EC9) The system of EC8, wherein the executing is one of: unconditional,non-selective, static, and a-priori determined.

EC10) The system of EC8, wherein the executing is one of: conditional,selective, dynamic, and not a-priori determined.

EC11) The system of EC1, wherein the floating-point operation comprisesone of floating-point addition, floating-point subtraction,floating-point multiplication, floating-point division, floating-pointreciprocal, floating-point comparison, floating-point absolute value,floating-point negation, floating-point maximum, floating-point minimum,floating-point elementary functions, floating-point square root,floating-point logarithm, floating-point exponentiation, floating-pointsine, floating-point cosine, floating-point tangent, floating-pointarctangent, and floating-point multiply-accumulate.

EC12) The system of EC1, wherein the floating-point operation comprisesfloating-point conversion to a different floating-point format.

EC13) The system of EC1, wherein the floating-point operation comprisesfloating-point conversion from integer.

EC14) The system of EC1, wherein the floating-point operation comprisesfloating-point conversion to integer.

EC15) The system of EC1, wherein the means for retaining and the meansfor performing collectively correspond to one or more hardware units ofa programmable processor that is one of a plurality of like programmableprocessors fabricated on a wafer in accordance with wafer-scaleintegration; and further comprising a datacenter element comprising thewafer and enabled to perform neural network processing.

EC16) A system comprising:

-   -   means for retaining a control resource; and    -   means for performing floating-point operations in accordance        with at least two of a plurality of programmable exponent bias        values mutually exclusively retainable by the control resource.

EC17) The system of EC16, wherein the means for performing comprisesmeans for producing at least some floating-point results comprisingrespective biased exponents that are biased in accordance with thecontrol resource.

EC18) The system of EC16, further comprising means for executing aninstruction to change the retained programmable exponent bias value froma first value to a second value.

EC19) The system of EC18, wherein the executing is one of:unconditional, non-selective, static, and a-priori determined.

EC20) The system of EC18, wherein the executing is one of: conditional,selective, dynamic, and not a-priori determined.

EC21) The system of EC18, further comprising a hardware registercomprising the means for retaining.

EC22) The system of EC16, wherein the means for retaining and the meansfor performing collectively correspond to one or more hardware units ofa programmable processor that is one of a plurality of like programmableprocessors fabricated on a wafer in accordance with wafer-scaleintegration; and further comprising a datacenter element comprising thewafer and enabled to perform neural network processing.

EC23) A system comprising:

-   -   a programmable processor comprising        -   means for retaining a control resource,        -   means for executing at least one change control resource            instruction, and        -   means for performing floating-point operations responsive to            one or more floating-point instructions;    -   wherein the control resource is changeable responsive to the        means for executing the at least one change control resource        instruction;    -   wherein the means for performing is in accordance with at least        two distinct ones of a plurality of programmable exponent bias        values mutually exclusively retainable by the control resource;    -   wherein the means for performing is compatible with interpreting        at least some floating-point operands of the floating-point        operations as having respective biased exponents that are biased        in accordance with the control resource; and    -   wherein the means for performing comprises means for producing        at least some floating-point results of the floating-point        operations as comprising respective biased exponents that are        biased in accordance with the control resource.

EC24) The system of EC23, wherein the programmable processor furthercomprises a hardware register comprising the means for retaining, andthe at least one change control resource instruction is a load hardwareregister instruction.

EC25) The system of EC23, wherein the means for retaining ismemory-mapped and the at least one change control resource instructionis a memory store instruction.

EC26) The system of EC23, wherein the programmable processor furthercomprises means to execute one or more instructions to unconditionally,to non-selectively, to statically, and/or to a-priori execute a loadcontrol resource instruction to change the control resource to a newvalue.

EC27) The system of EC23, wherein the programmable processor furthercomprises means to execute one or more instructions to conditionally, toselectively, to dynamically, and/or to not a-priori execute a loadcontrol resource instruction to change the control resource to a newvalue.

EC28) The system of EC23, where the programmable processor is one of aplurality of like programmable processors fabricated on a wafer inaccordance with wafer-scale integration, and a datacenter elementenabled to perform neural network processing comprises the wafer.

EC29) A system comprising:

-   -   a homogeneous fabric of programmable processing elements        comprising a first portion of programmable processing elements        and a second portion of programmable processing elements;    -   wherein the first portion of programmable processing elements        comprises a means for performing a first plurality of        floating-point operations in accordance with a means for        retaining a first programmable exponent bias;    -   wherein the second portion of programmable processing elements        comprises a means for performing a second plurality of        floating-point operations in accordance with a means for        retaining a second programmable exponent bias;    -   wherein the first portion of programmable processing elements        comprises a means for executing a first instruction to set the        means for retaining the first programmable exponent bias to a        first value; and    -   wherein the second portion of programmable processing elements        comprises a means for executing a second instruction to set the        means for retaining the second programmable exponent bias to a        second value.

EC30) The system of EC29, wherein at least some of the first pluralityof floating-point operations interpret operands and compute results inaccordance with the first programmable exponent bias, and wherein atleast some of the second plurality of floating-point operationsinterpret operands and compute results in accordance with the secondprogrammable exponent bias.

EC31) The system of EC29, wherein the first plurality of floating-pointoperations and the second plurality of floating-point operations are inaccordance with neural network processing.

EC32) The system of EC29, wherein the homogeneous fabric is fabricatedvia wafer-scale integration.

EC33) The system of EC29, wherein the homogeneous fabric is fabricatedon a wafer in accordance with wafer-scale integration, and a datacenterelement enabled to perform neural network processing comprises thewafer.

EC34) A system comprising:

-   -   a programmable processor comprising        -   means for executing instances of at least one load control            resource instruction to retain a programmable exponent bias            in a control resource, and        -   means for executing instances of floating-point instructions            in accordance with one or more floating-point values, at            least some of which comprise respective biased exponents,            the respective biased exponents are biased in accordance            with the programmable exponent bias, and the floating-point            values comprise one or more floating-point operands and one            or more floating-point results; and    -   wherein an instruction set architecture comprises the at least        one load control resource instruction and the floating-point        instructions.

EC35) The system of EC34, wherein the means for executing instances offloating-point instructions is enabled to execute a first portion of theinstances of floating-point instructions with the programmable exponentbias being a first value and is enabled to execute a second portion ofthe instances of floating-point instructions with the programmableexponent bias being a second value, and the first value and the secondvalue are distinct.

EC36) The system of EC35, wherein at least one of the instances of thefirst portion and at least one of the instances of the second portionhave a same instruction opcode value.

EC37) The system of EC35, wherein at least one of the instances of thefirst portion and at least one of the instances of the second portionare respective instances of a floating-point multiply-accumulateinstruction.

EC38) The system of EC34, wherein the control resource is a controlregister and the at least one load control resource instruction is aload control register instruction.

EC39) The system of EC34, wherein the control resource is memory-mappedand the at least one load control resource instruction is a memory storeinstruction.

EC40) The system of EC34, wherein the programmable processor is one of aplurality of like programmable processors of a homogenous fabric ofprogrammable processors.

EC41) The system of EC40, wherein the homogenous fabric is enabled toperform neural network processing.

EC42) The system of EC40, wherein the homogenous fabric is fabricatedvia wafer-scale integration.

EC43) The system of EC40, wherein the homogenous fabric is fabricated ona wafer in accordance with wafer-scale integration, and a datacenterelement enabled to perform neural network processing comprises thewafer.

EC44) A method comprising:

-   -   retaining a programmable exponent bias;    -   performing a floating-point operation in accordance with the        programmable exponent bias; and    -   wherein the performing is in accordance with interpreting one or        more floating-point operands of the floating-point operation        according to the programmable exponent bias and further in        accordance with producing one or more floating-point results        according to the programmable exponent bias.

EC45) The method of EC44, wherein the performing comprises accessing atleast one of the floating-point operands, and the at least one of thefloating-point operands comprises a biased exponent biased in accordancewith the programmable exponent bias.

EC46) The method of EC44, wherein at least one of the floating-pointresults comprises a biased exponent biased in accordance with theprogrammable exponent bias.

EC47) The method of EC45 or EC46, wherein the number of bits of thebiased exponent is independent of the programmable exponent bias.

EC48) The method of EC44, further comprising executing a programmedinstruction to set the programmable exponent bias to a particular value.

EC49) The method of EC48, wherein the programmed instruction is a firstprogrammed instruction, the performing is responsive to a secondprogrammed instruction, and the first programmed instruction and thesecond programmed instruction are executable by a same programmableprocessor.

EC50) The method of EC44, wherein the floating-point operation is afirst floating-point operation and the performing the firstfloating-point operation is with the programmable exponent bias having afirst value; and further comprising performing a second floating-pointoperation in accordance with the programmable exponent bias having asecond value distinct from the first value.

EC51) The method of EC50, further comprising executing a programmedinstruction to set the programmable exponent bias from the first valueto the second value.

EC52) The method of EC51, wherein the executing is one of:unconditional, non-selective, static, and a-priori determined.

EC53) The method of EC51, wherein the executing is one of: conditional,selective, dynamic, and not a-priori determined.

EC54) The method of EC44, wherein the floating-point operation comprisesone of floating-point addition, floating-point subtraction,floating-point multiplication, floating-point division, floating-pointreciprocal, floating-point comparison, floating-point absolute value,floating-point negation, floating-point maximum, floating-point minimum,floating-point elementary functions, floating-point square root,floating-point logarithm, floating-point exponentiation, floating-pointsine, floating-point cosine, floating-point tangent, floating-pointarctangent, and floating-point multiply-accumulate.

EC55) The method of EC44, wherein the floating-point operation comprisesfloating-point conversion to a different floating-point format.

EC56) The method of EC44, wherein the floating-point operation comprisesfloating-point conversion from integer.

EC57) The method of EC44, wherein the floating-point operation comprisesfloating-point conversion to integer.

EC58) The method of EC44, wherein the retaining and the performing arerealized via one or more hardware units of a programmable processor thatis one of a plurality of like programmable processors fabricated on awafer in accordance with wafer-scale integration, and a datacenterelement enabled to perform neural network processing comprises thewafer.

EC59) A method comprising:

-   -   performing first one or more floating-point operations in        accordance with a programmable exponent bias having a first        value; and performing second one or more floating-point        operations in accordance with the programmable exponent bias        having a second value.

EC60) The method of EC59, wherein the first one or more floating-pointoperations and the second one or more floating-point operations are inaccordance with respective biased exponents of a same number of bits.

EC61) The method of EC59, further comprising executing an instruction tochange the programmable exponent bias from the first value to the secondvalue.

EC62) The method of EC61, wherein the executing is one of:unconditional, non-selective, static, and a-priori determined.

EC63) The method of EC61, wherein the executing is one of: conditional,selective, dynamic, and not a-priori determined.

EC64) The method of EC61, wherein the programmable exponent bias isstored in a hardware register.

EC65) The method of EC64, wherein the hardware register is comprised ina floating-point unit enabled to perform the first one or morefloating-point operations and further enabled to perform the second oneor more floating-point operations.

EC66) The method of EC59, where the method is carried out by one or morehardware units of a programmable processor that is one of a plurality oflike programmable processors fabricated on a wafer in accordance withwafer-scale integration, and a datacenter element enabled to performneural network processing comprises the wafer.

EC67) A method comprising:

-   -   first performing a first floating-point operation using at least        a first operand comprising a first operand biased exponent        biased in accordance with a first exponent bias, the first        performing compatible with interpreting the first operand biased        exponent in accordance with the first exponent bias, the first        performing comprising providing a first result comprising a        first result biased exponent biased in accordance with the first        exponent bias;    -   executing an instruction to change a retained value from the        first exponent bias to a second exponent bias; and    -   second performing a second floating-point operation using at        least a second operand comprising a second operand biased        exponent in accordance with the second exponent bias, the second        performing compatible with interpreting the second operand        biased exponent in accordance with the second exponent bias, the        second performing comprising providing a second result        comprising a second result biased exponent biased in accordance        with the second exponent bias.

EC68) The method of EC67, wherein:

-   -   a programmable processor enabled to execute programmed        instructions comprises a floating-point unit and a control        register,    -   the floating-point unit is enabled to execute a first        floating-point instruction and the first performing is        responsive to the execution of the first floating-point        instruction,    -   the floating-point unit is enabled to execute a second        floating-point instruction and the second performing is        responsive to the execution of the second floating-point        instruction, and    -   the retained value is held in the control register.

EC69) The method of EC68, wherein the first floating-point instructionand the second floating-point instruction are two distinct instances ofa same floating-point instruction.

EC70) The method of EC68, wherein the execution of the firstfloating-point instruction and the execution of the secondfloating-point instruction are respectively responsive to respectivefetching of a same instruction from a same memory location.

EC71) The method of EC68, wherein the execution of the firstfloating-point instruction and the execution of the secondfloating-point instruction are respectively responsive to respectivefetching of identically coded instructions from respective differentmemory locations.

EC72) The method of EC68, wherein the first floating-point instructionand the second floating-point instruction are two differentfloating-point instructions having distinct floating-point opcodes.

EC73) The method of EC67, wherein the first operand biased exponent andthe second operand biased exponent are a same number of bits.

EC74) The method of EC67, wherein the first performing and the secondperforming are via a floating-point hardware unit.

EC75) The method of EC74, wherein the retained value is held in acontrol register, and the floating-point hardware unit comprises thecontrol register.

EC76) The method of EC67, wherein the executing is one of:unconditional, non-selective, static, and a-priori determined.

EC77) The method of EC67, wherein the executing is one of: conditional,selective, dynamic, and not a-priori determined.

EC78) The method of EC68, where the programmable processor is one of aplurality of like programmable processors fabricated on a wafer inaccordance with wafer-scale integration, and a datacenter elementenabled to perform neural network processing comprises the wafer.

EC79) A method comprising:

-   -   in a homogeneous fabric of programmable processing elements,        performing a first plurality of floating-point operations in        accordance with a first programmable exponent bias via a first        portion of the homogenous fabric, and performing a second        plurality of floating-point operations in accordance with a        second programmable exponent bias via a second portion of the        homogenous fabric;    -   in the first portion of the homogenous fabric, executing a first        instruction to set a first register to the first programmable        exponent bias; and    -   in the second portion of the homogenous fabric, executing a        second instruction to set a second register to the second        programmable exponent bias.

EC80) The method of EC79, wherein the first register is comprised in thefirst portion of the homogenous fabric and at least some of the firstplurality of floating-point operations interpret operands and computeresults in accordance with the first register, and wherein the secondregister is comprised in the second portion of the homogenous fabric andat least some of the second plurality of floating-point operationsinterpret operands and compute results in accordance with the secondregister.

EC81) The method of EC79, wherein the first plurality of floating-pointoperations and the second plurality of floating-point operations are inaccordance with neural network processing.

EC82) The method of EC79, wherein the homogeneous fabric is fabricatedvia wafer-scale integration.

EC83) The method of EC79, wherein the homogeneous fabric is fabricatedon a wafer in accordance with wafer-scale integration, and a datacenterelement enabled to perform neural network processing comprises thewafer.

EC84) A method comprising:

-   -   executing an instance of at least one load control resource        instruction to retain a programmable exponent bias in a control        resource;    -   executing instances of floating-point instructions in accordance        with one or more floating-point values, at least some of which        comprise respective biased exponents, the respective biased        exponents are biased in accordance with the programmable        exponent bias, and the floating-point values comprise one or        more floating-point operands and one or more floating-point        results; and    -   wherein a programmable processor comprises the control resource        and a floating-point unit enabled to perform the executing        instances of floating-point instructions, and the programmable        processor is enabled to execute instructions according to an        instruction set architecture comprising the at least one load        control resource instruction and the floating-point        instructions.

EC85) The method of EC84, wherein the executing instances offloating-point instructions comprises executing a first portion of theinstances of floating-point instructions with the programmable exponentbias being a first value and executing a second portion of the instancesof floating-point instructions with the programmable exponent bias beinga second value, and the first value and the second value are distinct.

EC86) The method of EC85, wherein at least one of the instances of thefirst portion and at least one of the instances of the second portionhave a same instruction opcode value.

EC87) The method of EC85, wherein at least one of the instances of thefirst portion and at least one of the instances of the second portionare respective instances of a floating-point multiply-accumulateinstruction.

EC88) The method of EC84, wherein the control resource is a controlregister and the at least one load control resource instruction is aload control register instruction.

EC89) The method of EC84, wherein the control resource is memory-mappedand the at least one load control resource instruction is a memory storeinstruction.

EC90) The method of EC84, wherein the programmable processor is one of aplurality of like programmable processors of a homogenous fabric ofprogrammable processors.

EC91) The method of EC90, wherein the homogenous fabric is enabled toperform neural network processing.

EC92) The method of EC90, wherein the homogenous fabric is fabricatedvia wafer-scale integration.

EC93) The method of EC90, wherein the homogenous fabric is fabricated ona wafer in accordance with wafer-scale integration, and a datacenterelement enabled to perform neural network processing comprises thewafer.

EC94) An apparatus comprising:

-   -   a programmable processor enabled to execute instructions, the        instructions comprising floating-point instructions and a load        control resource instruction;    -   a control resource of the programmable processor enabled to        receive an exponent bias responsive to an operation        corresponding to the load control resource instruction; and    -   a floating-point unit of the programmable processor enabled to        perform floating-point operations corresponding to the        floating-point instructions, the performing floating-point        operations being in accordance with floating-point operands and        floating-point results each comprising a respective biased        exponent, the performing floating-point operations being        responsive to the exponent bias, and compatible, according to        the exponent bias, with interpreting the biased exponents of the        floating-point operands and producing the biased exponents of        the floating-point results.

EC95) The apparatus of EC94, wherein the control resource is a controlregister and the load control resource instruction is a load controlregister instruction.

EC96) The apparatus of EC94, wherein the control resource ismemory-mapped and the load control resource instruction is a memorystore instruction.

EC97) The apparatus of EC94, wherein the programmable processor is oneof a plurality of like programmable processors of a homogenous fabric ofprogrammable processors.

EC98) The apparatus of EC97, wherein the homogenous fabric is enabled toperform neural network processing.

EC99) The apparatus of EC97, wherein the homogenous fabric is fabricatedvia wafer-scale integration.

EC100) The apparatus of EC94, wherein the exponent bias and therespective biased exponents are a same number of bits.

EC101) The apparatus of EC94, wherein:

-   -   the control resource is a first control resource,    -   the load control resource instruction is a first load control        resource instruction,    -   the instructions further comprise a second load control resource        instruction,    -   the apparatus further comprises a second control resource of the        programmable processor enabled to receive an exponent size        specifier responsive to an operation corresponding to the second        load control resource instruction,    -   the floating-point unit is further enabled to perform the        floating-point operations responsive to the exponent size        specifier specifying one of a plurality of floating-point        formats, and a first of the plurality of floating-point formats        comprises a biased exponent of N bits and a mantissa of M bits,        and a second of the plurality of floating-point formats        comprises a biased exponent of (N+delta) bits and a mantissa of        (M−delta) bits, and    -   the performing floating-point operations is further compatible,        according to the exponent size specifier, with interpreting the        biased exponents of the floating-point operands and producing        the biased exponents of the floating-point results.

EC102) The apparatus of EC101, wherein the first control resource andthe second control resource are implemented in a same hardware register.

EC103) The apparatus of EC101, wherein delta is one.

EC104) The apparatus of EC101, wherein N is five and M is ten.

EC105) The apparatus of EC94, wherein:

-   -   the control resource is a first control resource,    -   the load control resource instruction is a first load control        resource instruction,    -   the instructions further comprise a second load control resource        instruction,    -   the apparatus further comprises a second control resource of the        programmable processor enabled to receive a maximum biased        exponent normal specifier responsive to an operation        corresponding to the second load control resource instruction,    -   the floating-point unit is further enabled to perform the        floating-point operations responsive to the maximum biased        exponent normal specifier specifying one of a plurality of        floating-point modes, and a first of the plurality of        floating-point modes comprises using a maximum biased exponent        to represent a biased exponent, and    -   the performing floating-point operations is further compatible,        according to the maximum biased exponent normal specifier, with        interpreting the biased exponents of the floating-point operands        and producing the biased exponents of the floating-point        results.

EC106) The apparatus of EC105, wherein the first control resource andthe second control resource are implemented in a same hardware register.

EC107) The apparatus of EC105, wherein:

-   -   the instructions further comprise a third load control resource        instruction,    -   the apparatus further comprises a third control resource of the        programmable processor enabled to receive a rounding mode        specifier responsive to an operation corresponding to the third        load control resource instruction, and    -   the floating-point unit is further enabled to perform the        floating-point operations in accordance with one of a plurality        of rounding modes specified by the rounding mode specifier, and        at least one of the rounding modes comprises saturating the        floating-point results to a value comprising a maximum biased        exponent.

EC108) The apparatus of EC107, wherein the first control resource, thesecond control resource, and the third control resource are implementedin a same hardware register.

EC109) The apparatus of EC105, wherein a second of the plurality offloating-point modes comprises using the maximum biased exponent torepresent at least one of: infinite numbers and IEEE compatible NaN.

EC110) The apparatus of EC94, wherein:

-   -   the control resource is a first control resource,    -   the load control resource instruction is a first load control        resource instruction,    -   the instructions further comprise a second load control resource        instruction,    -   the apparatus further comprises a second control resource of the        programmable processor enabled to receive a zero biased exponent        specifier responsive to an operation corresponding to the second        load control resource instruction,    -   the floating-point unit is further enabled to perform the        floating-point operations responsive to the zero biased exponent        specifier specifying one of a plurality of floating-point modes,        and at least one of the floating-point modes comprises using a        zero biased exponent to represent a biased exponent, and    -   the performing floating-point operations is further compatible,        according to the zero biased exponent specifier, with        interpreting the biased exponents of the floating-point operands        and producing the biased exponents of the floating-point        results.

EC111) The apparatus of EC110, wherein the first control resource andthe second control resource are implemented in a same hardware register.

EC112) The apparatus of EC110, wherein:

-   -   the instructions further comprise a third load control resource        instruction,    -   the apparatus further comprises a third control resource of the        programmable processor enabled to receive a flush-to-zero        specifier responsive to an operation corresponding to the third        load control resource instruction, and    -   the floating-point unit is further enabled to perform the        floating-point operations responsive to the flush-to-zero        specifier specifying a flush-to-zero mode, and the flush-to-zero        mode comprises flushing subnormal results to zero.

EC113) The apparatus of EC112, wherein the first control resource, thesecond control resource, and the third control resource are implementedin a same hardware register.

EC114) The apparatus of EC110, wherein a second of the plurality offloating-point modes comprises using the zero biased exponent torepresent at least subnormal floating-point numbers.

EC115) The apparatus of EC94, wherein the programmable processor is oneof a plurality of like programmable processors fabricated on a wafer inaccordance with wafer-scale integration, and a datacenter elementenabled to perform neural network processing comprises the wafer.

EC116) An apparatus comprising:

-   -   a programmable processor comprising a control resource and a        floating-point unit;    -   wherein the floating-point unit is enabled to perform        floating-point operations in accordance with at least two of a        plurality of programmable exponent bias values retainable by the        control resource; and    -   wherein the performing comprises producing at least some        floating-point results comprising respective biased exponents        that are biased in accordance with the control resource.

EC117) The apparatus of EC116, wherein the programmable processor isenabled to execute an instruction to change the control resource to anew value.

EC118) The apparatus of EC117, wherein the programmable processor isfurther enabled to execute floating-point instructions that theperforming is responsive to.

EC119) The apparatus of EC116, wherein the programmable processor isenabled to execute one or more instructions to unconditionally, tonon-selectively, to statically, and/or to a-priori execute a loadcontrol resource instruction to change the control resource to a newvalue.

EC120) The apparatus of EC116, wherein the programmable processor isenabled to execute one or more instructions to conditionally, toselectively, to dynamically, and/or to not a-priori execute a loadcontrol resource instruction to change the control resource to a newvalue.

EC121) The apparatus of EC116, wherein the programmable processor is oneof a plurality of like programmable processors fabricated on a wafer inaccordance with wafer-scale integration, and a datacenter elementenabled to perform neural network processing comprises the wafer.

EC122) An apparatus comprising:

-   -   a programmable processor comprising a control resource and a        floating-point unit;    -   wherein the programmable processor is enabled to execute at        least one change control resource instruction and to execute one        or more floating-point instructions;    -   wherein the control resource is changeable responsive to the        execution of the at least one change control resource        instruction;    -   wherein the floating-point unit is enabled to perform        floating-point operations responsive to the execution of the        floating-point instructions;    -   wherein the performing is in accordance with at least two        distinct ones of a plurality of programmable exponent bias        values mutually exclusively retainable by the control resource;    -   wherein the performing is compatible with interpreting at least        some floating-point operands of the floating-point operations as        having respective biased exponents that are biased in accordance        with the control resource; and    -   wherein the performing comprises producing at least some        floating-point results of the floating-point operations as        comprising respective biased exponents that are biased in        accordance with the control resource.

EC123) The apparatus of EC122, wherein the programmable processorcomprises a hardware register comprising the control resource, and theat least one change control resource instruction is a load hardwareregister instruction.

EC124) The apparatus of EC122, wherein the control resource ismemory-mapped and the at least one change control resource instructionis a memory store instruction.

EC125) The apparatus of EC122, wherein the execution of the at least onechange control resource instruction is one or more of: unconditional,non-selective, static, and determined a-priori.

EC126) The apparatus of EC122, wherein the execution of the at least onechange control resource instruction is one or more of: conditional,selective, dynamic, and not determined a-priori.

EC127) The apparatus of EC122, wherein the programmable processor is oneof a plurality of like programmable processors fabricated on a wafer inaccordance with wafer-scale integration, and a datacenter elementenabled to perform neural network processing comprises the wafer.

EC128) An apparatus comprising:

-   -   a homogeneous fabric comprising a plurality of programmable        processing elements;    -   wherein a first portion of the programmable processing elements        is enabled to perform first floating-point operations in        accordance with a first programmable exponent bias retained in a        first control resource;    -   wherein a second portion of the programmable processing elements        is enabled to perform second floating-point operations in        accordance with a second programmable exponent bias retained in        a second control resource and that optionally has a value that        is distinct from that of the first programmable exponent bias;        and    -   wherein the apparatus is enabled to manage the performing the        first floating-point operations and the performing the second        floating-point operations to be at least partially concurrent.

EC129) The apparatus of EC128, wherein the first portion of theprogrammable processing elements is further enabled to interpretoperands and compute results in accordance with the first programmableexponent bias, and the second portion of the programmable processingelements is further enabled to interpret operands and compute results inaccordance with the second programmable exponent bias.

EC130) The apparatus of EC128, wherein the first portion and the secondportion are two portions of a plurality of portions of the programmableprocessing elements, and each of the plurality of portions has arespective single processing element enabled to perform respectivefloating-point operations in accordance with a respective singleprogrammable bias retained in a respective single control resource.

EC131) The apparatus of EC128, wherein the first control resource is ahardware register of one of the programmable processing elements of thefirst portion and the second control resource is a hardware register ofone of the programmable processing elements of the second portion.

EC132) The apparatus of EC128, wherein the first control resource andthe second control resource are memory-mapped.

EC133) The apparatus of EC128, wherein the homogeneous fabric isfabricated on a wafer in accordance with wafer-scale integration, and adatacenter element enabled to perform neural network processingcomprises the wafer.

EC134) An apparatus comprising:

-   -   a programmable processor enabled to execute instructions        according to an instruction set architecture, the instruction        set architecture comprising one or more floating-point        instructions and at least one load control resource instruction;    -   a control resource of the programmable processor enabled,        responsive to execution of instances of the at least one load        control resource instruction, to receive a programmable exponent        bias; and    -   a floating-point unit of the programmable processor enabled to        execute instances of the floating-point instructions in        accordance with one or more floating-point values at least some        of which comprise respective biased exponents, the respective        biased exponents biased in accordance with the programmable        exponent bias, the floating-point values comprising one or more        floating-point operands and one or more floating-point results.

EC135) The apparatus of EC134, wherein the floating-point unit isfurther enabled to execute at least a first portion of the instances ofthe floating-point instructions in accordance with the programmableexponent bias being a first value and to execute at least a secondportion of the instances of the floating-point instructions inaccordance with the programmable exponent bias being a second value, andthe first value and the second value are distinct.

EC136) The apparatus of EC135, wherein at least one of the instances ofthe first portion and at least one of the instances of the secondportion have a same instruction opcode value.

EC137) The apparatus of EC135, wherein at least one of the instances ofthe first portion and at least one of the instances of the secondportion are respective instances of a floating-point multiply-accumulateinstruction.

EC138) The apparatus of EC134, wherein the control resource is a controlregister and the at least one load control resource instruction is aload control register instruction.

EC139) The apparatus of EC134, wherein the control resource ismemory-mapped and the at least one load control resource instruction isa memory store instruction.

EC140) The apparatus of EC134, wherein the programmable processor is oneof a plurality of like programmable processors of a homogenous fabric ofprogrammable processors.

EC141) The apparatus of EC140, wherein the homogenous fabric is enabledto perform neural network processing.

EC142) The apparatus of EC140, wherein the homogenous fabric isfabricated via wafer-scale integration.

Selected Embodiment Details

Embodiments relating to neural network training and inference,comprising deep learning accelerator hardware elements and softwareelements are described herein (see, e.g., FIGS. 1-4 and section “DeepLearning Accelerator Overview”). The deep learning accelerator compriseshardware processing elements (see, e.g., FIGS. 5-8 and sections “FabricOverview” and “Processing Element: Compute Element and Router”). Thedeep learning accelerator implements and/or uses various techniques suchas tasks, including task initiation and task blocking/unblocking (see,e.g., FIGS. 9A-9C and sections “Task Initiation” and “Task Block andUnblock”), neuron to processing element mapping and associated dataflow(see, e.g., FIGS. 10A-10B and section “High-Level Dataflow”), task statemachines and closeouts (see, e.g., FIGS. 11-12 and section “ExampleWorkload Mapping and Exemplary Tasks”), wavelet processing (see, e.g.,FIGS. 13A-16 and section “Wavelets”), neuron smearing (see, e.g., FIGS.17-20 and section “Neuron Smearing”), fabric vectors, memory vectors,and associated data structure descriptors (see, e.g., FIGS. 21A-24 andsection “Vectors and Data Structure Descriptors”), and instructionformats (see, e.g., FIGS. 25A-25C and section “Instruction Formats”).The hardware processing elements of the deep learning accelerator areenabled to perform work when stalled (see, e.g., FIG. 26 and section“Microthreading”). The deep learning accelerator is usable in a varietyof scenarios (see, e.g., FIGS. 27A-28E and section “Deep LearningAccelerator Example Uses”. The deep learning accelerator optionallyimplements floating-point operations with one or more of optionalstochastic rounding, optional programmable exponent bias, and optionaland/or selective data formats with different exponent precision (see,e.g., FIGS. 29, 30A-E, and 31-32; and section “Floating-Point OperatingContext and Stochastic Rounding Operation”). The deep learningaccelerator is scalable for large deep neural networks (see, e.g.,section “Scalability for Large Deep Neural Networks”). The deep learningaccelerator is contemplated in various embodiments (see, e.g., section“Other Embodiment Details”). The deep learning accelerator is variouslyimplementable (see, e.g., section “Example Implementation Techniques”).

Deep Learning Accelerator Overview

FIG. 1 illustrates selected details of an embodiment of a system forneural network training and inference, using a deep learningaccelerator, as Neural Network System 100. Conceptually a neural networkis trained using the deep learning accelerator. One or more results ofthe training (e.g., weights) are then used for inferences. For example,the training comprises mapping neurons of the neural network onto PEs ofthe deep learning accelerator. Then training data is applied to the PEs.The PEs process the training data (e.g., via forward, delta, and chainpasses) and update weights until the training is complete. Then theweights are used for inference.

Referring to the figure, Deep Learning Accelerator 120 comprises FPGAs121 and PEs 122, enabled to communicate with each other, as illustratedby Coupling 123. Placement Server(s) 150, (comprising CPUs 151 and CRM152) is coupled to Connection Server(s) 160 (comprising CPUs 161, CRM162, and NICs 164) via LAN 111. Connection Server(s) 160 is enabled tocommunicate with FPGAs 121 via NICs 164 and 100 Gb 112. AutonomousVehicle 130 comprises CPUs 131, CRM 132, IEs 133, and Camera 135. CellPhone 140 comprises CPUs 141, CRM 142, IEs 143, and Camera 145.

Internet 180 provides for coupling (not explicitly illustrated) betweenany combination of Placement Server(s) 150, Connection Server(s) 160,Autonomous Vehicle 130, and/or Cell Phone 140, according to variousembodiments and/or usage scenarios.

Dashed-arrow Placements 113 conceptually indicates placement informationcommunicated from Placement Server(s) 150 to PEs 122 (e.g., via LAN 111,Connection Server(s) 160/NICs 164, 100 Gb 112, FPGAs 121, and Coupling123). In some embodiments and/or usage scenarios, Placements 113 isimplicit, reflected in initialization information provided to routerelements of PEs 122 and compute elements of PEs 122. In some embodimentsand/or usage scenarios, a portion of initialization information ofPlacements 113 is provided to FPGAs 121 to configure elements of FPGAs121 for operation with PEs 122.

Dashed-arrow Weights 114 and dashed-arrow Weights 115 conceptuallyindicate weight information communicated from PEs 122 respectively toAutonomous Vehicle 130 and Cell Phone 140 (e.g., via Coupling 123, FPGAs121, 100 Gb 112, Connection Server(s) 160/NICs 164 and Internet 180). Insome embodiments and/or usage scenarios, the weight information is anyone or more of all or any portions of weight information as directlyproduced as a result of training, a sub-sampling thereof, a quantizationthereof, and/or other transformations thereof.

Deep Learning Accelerator 120 is enabled to perform training of neuralnetworks, such as by computing weights in response to placementinformation and training information received via 100 Gb 112. DeepLearning Accelerator 120 is further enabled to, upon trainingcompletion, provide the weights as results via 100 Gb 112. The weightsare then usable for inference, such as in Autonomous Vehicle 130 and/orin Cell Phone 140. PEs 122 comprises a relatively large number of PEs(e.g., 10,000 or more) each enabled to independently perform routing andcomputations relating to training. In some embodiments and/or usagescenarios, PEs 122 is implemented via wafer-scale integration, such asrespective pluralities of PEs implemented on respective dice of a singlewafer. FPGAs 121 is enabled to interface PEs 122 to information providedvia 100 Gb 112. The interfacing includes conversion to/from modifiedEthernet frames from/to Wavelets, as communicated on Coupling 123.

Placement Server(s) 150 is enabled to programmatically determineplacements of neurons (e.g., as indicated by Placements 113) via one ormore placement programs. The placement programs are stored in CRM 152and executed by CPUs 151. The placement information is communicated toConnection Server(s) 160 via LAN 111. An example of a placement is amapping of logical neurons of a neural network onto physical memory andexecution hardware resources (e.g., PEs 122).

Connection Server(s) 160 is enabled to communicate with FPGAs 121 andindirectly with PEs 122 via FPGAs 121/Coupling 123, via NICs 164 andprogrammed control thereof via driver programs. In various embodimentsand/or usage scenarios, the communication comprises placementinformation (e.g., from Placement Server(s) 150), training information(e.g., from sources not illustrated but accessible via Internet 180)and/or results of training (e.g., weights from PEs 122). The driverprograms are stored in CRM 162 and executed by CPUs 161.

Autonomous Vehicle 130 is enabled to use Weights 114 to performinferences using IEs 133 as programmatically controlled and/or assistedby CPUs 131 executing programs stored in CRM 132. The inferences areoptionally and/or selectively performed using information obtained fromCamera 135. For example, a car is operable as an autonomous vehicle. Thecar comprises cameras enabled to provide video to an inference engine.The inference engine is enabled to recognize objects related tonavigating the car, such as traffic lanes, obstructions, and otherobjects. The car is enabled to navigate using results of the objectrecognition. Any combination of the providing, the recognizing, and thenavigating are controlled and/or performed at least in part via one ormore CPUs executing programs stored in a CRM.

Cell Phone 140 is enabled to use Weights 115 to perform inferences usingIEs 143 as programmatically controlled and/or assisted by CPUs 141executing programs stored in CRM 142. The inferences are optionallyand/or selectively performed using information obtained from Camera 145.For example, the cell phone is operable to post tagged photos on asocial networking web site. The cell phone comprises a camera enabled toprovide image data to an inference engine. The inference engine isenabled to tag objects (e.g., by type such as ‘cat’, ‘dog’, and soforth, or by name such as ‘Bob’, ‘Mary’, and so forth) in the image. Thecell phone is enabled to post the image and results of the tagging tothe social networking web site. Any combination of the providing, thetagging, and the posting are controlled and/or performed at least inpart via one or more CPUs executing programs stored in a CRM.

In various embodiments and/or usage scenarios, all or any portions ofweight information determined via a deep learning accelerator ispost-processed outside of the accelerator before inference usage. Forexample, all or any portions of information represented by Weights 114and/or Weights 115, is processed in whole or in part by PlacementServer(s) 150 before inference usage by Autonomous Vehicle 130 and/orCell Phone 140. In various embodiments and/or usage scenarios, anexample of post-processing comprises quantizing Weights 114 and/orWeights 115 (e.g., converting from a floating-point number format to afixed-point number format). In various embodiments and/or usage models,Camera 135 and Camera 145 are respective examples of sensors thatprovide input to IEs 133 and IEs 143. Other examples of sensors arelocation sensors, orientation sensors, magnetic sensors, light sensors,and pressure sensors.

CPUs 151 comprises one or more CPUs that are compatible with respectiveinstruction set architectures. CPUs 151 is enabled to fetch and executeinstructions from CRM 152 in accordance with the instruction setarchitectures. CPUs 161 comprises one or more CPUs that are compatiblewith respective instruction set architectures. CPUs 161 is enabled tofetch and execute instructions from CRM 162 in accordance with theinstruction set architectures. In some embodiments, at least one of theinstruction set architectures of CPUs 151 is compatible with at leastone of the instruction set architectures of CPUs 161.

CPUs 131 comprises one or more CPUs that are compatible with respectiveinstruction set architectures. CPUs 131 is enabled to fetch and executeinstructions from CRM 132 in accordance with the instruction setarchitectures. CPUs 141 comprises one or more CPUs that are compatiblewith respective instruction set architectures. CPUs 141 is enabled tofetch and execute instructions from CRM 142 in accordance with theinstruction set architectures. In some embodiments, at least one of theinstruction set architectures of CPUs 131 is compatible with at leastone of the instruction set architectures of CPUs 141. In someembodiments, any one or more of CPUs 151, CPUs 161, CPUs 131, and CPUs141 have instruction set architectures that are compatible with eachother.

In some embodiments and/or usage scenarios, at least a respectiveportion of each of CRM 152 and CRM 162 CRM 132, and CRM 142, isnon-volatile and comprised of any one or more of flash memory, magneticmemory, optical memory, phase-change memory, and other non-volatilememory technology elements.

In various embodiments and/or usage scenarios, IEs 133 and/or IEs 143comprise one or more inference engines enabled to use weight informationas determined by Deep Learning Accelerator 120 (and indicatedconceptually by Weights 114 and/or Weights 115). In various embodimentsand/or usage scenarios, IEs 133 operates in conjunction with and/orunder control of programs executed by CPUs 131 and stored in CRM 132. Invarious embodiments and/or usage scenarios, IEs 143 operates inconjunction with and/or under control of programs executed by CPUs 141and stored in CRM 142. In various embodiments and/or usage scenarios,all or any portions of IEs 133 and/or IEs 143 are implemented viavarious combinations of HW and/or SW techniques. In some embodiments,all or any portions of functionality provided by IEs 133 and/or IEs 143is implemented using techniques such as implemented by and/or associatedwith Deep Learning Accelerator 120. In various embodiments and/or usagescenarios, all or any portions of IEs 133 and/or IEs 143 are variouslyimplemented via techniques comprising various combinations ofconventional CPUs, conventional GPUs, conventional DSPs, conventionalFPGAs, and specialized hardware.

In various embodiments, 100 Gb 112, is variously a 100 Gb Ethernetcoupling for sending standard Ethernet frames, a 100 Gb Ethernetcoupling for sending modified Ethernet frames, a 100 GB modifiedEthernet coupling for sending modified Ethernet frames, a 100 Gb serialcoupling of other-than Ethernet technology, or some other relativelyhigh-speed serial coupling.

In some embodiments and/or usage scenarios, Coupling 123 communicatesinformation as wavelets.

In various embodiments, LAN 111 is implemented using techniques such asEthernet, Fibre Channel, and/or other suitable interconnectiontechnologies.

In some embodiments and/or usage scenarios, Placement Server(s) 150 andConnection Server(s) 160 are implemented and/or operated as a combinedelement (e.g., sharing CPU, CRM, and/or NIC resources), as illustratedconceptually by Combined Server(s) 110. In some embodiments and/or usagescenarios, Placement Server(s) 150 and Connection Server(s) 160 arecoupled via Internet 180 rather than (or in addition to) LAN 111.

FIG. 2 illustrates selected details of an embodiment of softwareelements associated with neural network training and inference, using adeep learning accelerator, as Neural Network Software 200. PlacementServer(s) SW 210 comprises Neuron to PE Mapping SW 212, as well as otherelements not illustrated, according to embodiment. In variousembodiments and/or usage scenarios, all or any portions of PlacementServer(s) SW 210 is stored in CRM 152 and executable by CPUs 151 ofFIG. 1. One or more programs of Neuron to PE Mapping SW 212 enabledetermining placements of neurons of a neural network onto specific PEsof PEs 122 of FIG. 1.

Connection Server(s) SW 220 comprises 100 Gb NIC Driver 224, TrainingInfo Provider SW 225, and Weight Receiver SW 226, as well as otherelements not illustrated, according to embodiment. In variousembodiments and/or usage scenarios, all or any portions of ConnectionServer(s) SW 220 is stored in CRM 162 and executable by CPUs 161 ofFIG. 1. One or more programs of 100 Gb NIC Driver 224 enablecommunication between Connection Server(s) 160 and Deep LearningAccelerator 120, both of FIG. 1 (via NICs 164 and 100 Gb 112, also ofFIG. 1). One or more programs of Training Info Provider SW 225 enabledetermination of training information for application under control of100 Gb NIC Driver 224 for communication to Deep Learning Accelerator 120of FIG. 1 (via NICs 164 and 100 Gb 112). In various embodiments and/orusage scenarios, the training information is variously determined from,e.g., non-volatile storage accessible to Connection Server(s) 160 and/orInternet 180, both of FIG. 1. One or more programs of Weight Receiver SW226 enable receiving weight information under control of 100 Gb NICDriver 224 as determined by Deep Learning Accelerator 120 (via NICs 164and 100 Gb 112).

In various embodiments and/or usage scenarios, Misc SW on FPGAs 250conceptually represents SW executed by one or more CPUs comprised inFPGAs 121 of (FIG. 1). The CPUs of the FPGAs are, e.g., hard-codedduring manufacturing of one or more elements of FPGAs 121, and/orsoft-coded during initialization of one or more elements of FPGAs 121.In various embodiments and/or usage scenarios, all or any portions ofMisc SW on FPGAs 250 and/or a representation thereof is stored innon-volatile memory comprised in FPGAs 121 and/or accessible toConnection Server(s) 160. In various embodiments and/or usage scenarios,Misc SW on FPGAs 250 enables performing various housekeeping functions,such as relating to initialization and/or debugging of PEs 122 of FIG.1.

In various embodiments and/or usage scenarios, Task SW on PEs 260conceptually represents distributed SW executed as tasks on various PEsof PEs 122. In various embodiments and/or usage scenarios, all or anyportions of Task SW on PEs 260 and/or a representation thereof is storedin non-volatile memory comprised in PEs 122 and/or accessible toConnection Server(s) 160. In various embodiments and/or usage scenarios,Task SW on PEs 260 enables performing processing of training data suchas to determine weights of a neural network (e.g., via forward, delta,and chain passes).

Autonomous Vehicle SW 230 comprises Video Camera SW 232, InferenceEngine(s) SW 233, and Navigating SW 234, as well as other elements notillustrated, according to embodiment. In various embodiments and/orusage scenarios, all or any portions of Autonomous Vehicle SW 230 isstored in CRM 132 and executable by CPUs 131 of FIG. 1. One or moreprograms of Video Camera SW 232 enable controlling and/or operatingCamera 135 of FIG. 1 to provide video information to Inference Engine(s)SW 233. One or more programs of Inference Engine(s) SW 233 enablecontrolling and/or operating IEs 133 of FIG. 1 to determine navigationalinformation, such as objects to avoid and/or traffic lanes to follow,from the video information. One or more programs of Navigating SW 234enable navigating Autonomous Vehicle SW 230 in response to thenavigational information.

Cell Phone SW 240 comprises Still Camera SW 242, Inference Engine(s) SW243, Posting SW 244, as well as other elements not illustrated,according to embodiment. In various embodiments and/or usage scenarios,all or any portions of Cell Phone SW 240 is stored in CRM 142 andexecutable by CPUs 141 of FIG. 1. One or more programs of Still CameraSW 242 enable controlling and/or operating Camera 145 of FIG. 1 toprovide still image information to Inference Engine(s) SW 243. One ormore programs of Inference Engine(s) SW 243 enable controlling and/oroperating IEs 143 of FIG. 1 to determine tag information from the stillimage information. One or more programs of Posting SW 244 enable postingto a social networking web site in response to the still imageinformation and/or the tag information.

In various embodiments and/or usage scenarios, any one or more of SWcollections Placement Server(s) SW 210, Connection Server(s) SW 220,Autonomous Vehicle SW 230, and/or Cell Phone SW 240 optionally and/orselectively comprise one or more operating system elements, e.g., one ormore real-time operating systems, one or more non-real-time operatingsystems, and/or one or more other control programs to coordinateelements of each respective SW collection.

FIG. 3 illustrates selected details of an embodiment of processingassociated with training a neural network and performing inference usingthe trained neural network, using a deep learning accelerator, as NeuralNetwork Training/Inference 300. As illustrated, neurons of the neuralnetwork are placed, e.g., allocated and/or associated with specific PEresources in action 310. Then FPGA resources are initialized inpreparation for training of the neural network in action 320. Then thePE resources are initialized in preparation for training of the neuralnetwork in action 330.

After the FPGA resources and PE resources are initialized in preparationfor the training, training data is applied to the PEs in action 340. ThePE resources process the training data in action 350. Then a check ismade to determine if training is complete, e.g., because application ofthe training data is complete and/or one or more completion criteria aremet (such as an inference error below a predetermine bound) in action360. If not, then flow passes back to action 340 for application offurther training data. In some scenarios, the training does not completeand in some embodiments, control instead passes to another action (notillustrated) to enable changing, for example, hyperparameters of theneural network (e.g., any one or more of: adding layers of neurons,removing layers of neurons, changing connectivity between neurons,changing the batch size, and changing the learning rule). The changedneural network is then trained in accordance with actions 310, 320, 330,340, 350, and 360.

If training is complete, then flow continues to provide weights that areresults of the training for use in inferences in 370. In someembodiments and/or usage scenarios, the weights are quantized, e.g.,transformed to an integer data format. In some embodiments and/or usagescenarios, the integer data format is a reduced precision number format(e.g., 8-bit or 16-bit). The weights are then provided to one or moreinference engines, and used to make inferences in action 380.

In various embodiments and/or usage scenarios, the inference enginescorrespond to one or more inference applications, e.g., texttranslation, optical character recognition, image classification, facialrecognition, scene recognition for a self-driving car, speechrecognition, data analysis for high energy physics, and drug discovery.

In various embodiments and/or usage scenarios, the PE resourcescorrespond, e.g., to PEs 122 of FIG. 1, and the FPGAs resourcescorrespond, e.g., to FPGAs 121 of FIG. 1.

In various embodiments and/or usage scenarios, any one or more of all orany portions of actions of Neural Network Training/Inference 300 areperformed by and/or related to all or any portions of any one or moreelements of Neural Network System 100 of FIG. 1 and/or Neural NetworkSoftware 200 of FIG. 2. For example, all or any portions of action 310are performed by Placement Server(s) 150 via execution of Neuron to PEMapping SW 212. For another example, all or any portions of action 320are performed by Placement Server(s) 150 via execution of Neuron to PEMapping SW 212. For another example, all or any portions of action 330are performed by Placement Server(s) 150 via execution of Neuron to PEMapping SW 212. For another example, all or any portions of action 330are performed by PEs 122 via execution of Task SW on PEs 260. Foranother example, all or any portions of action 340 are performed byConnection Server(s) 160 via execution of Training Info Provider SW 225.For another example, all or any portions of action 350 are performed byPEs 122 via execution of Task SW on PEs 260. For another example, all orany portions of action 350 are performed by Combined Server(s) 110,Placement Server(s) 150 and/or Connection Server(s) 160. For anotherexample, all or any portions of 370 are performed by ConnectionServer(s) 160 via execution of Weight Receiver SW 226. For anotherexample, all or any portions of action 370 are performed by FPGAs 121via execution of Misc SW on FPGAs 250. For another example, all or anyportions of 380 are performed by IEs 133 such as under control ofInference Engine(s) SW 233. For another example, all or any portions ofaction 380 are performed by IEs 143 such as under control of InferenceEngine(s) SW 243.

In various embodiments and/or usage scenarios, any one or more of all orany portions of actions of Neural Network Training/Inference 300 areperformed in conjunction with communicating information between variouselements of Neural Network System 100 of FIG. 1. For example, variousactions of Neural Network Training/Inference 300 are performed at leastin part via NICs 164 and 100 Gb 112 communicating information betweenConnection Server(s) 160 and FPGAs 121. For another example, variousactions of Neural Network Training/Inference 300 are performed inconjunction with FPGAs 121 and Coupling 123 communicating informationbetween Connection Server(s) 160 and PEs 122. For another example,various actions of Neural Network Training/Inference 300 performed inconjunction with any one or more of Placement Server(s) 150, ConnectionServer(s) 160, Autonomous Vehicle 130, and Cell Phone 140 communicatinginformation as enabled at least in part by Internet 180.

FIG. 4 illustrates selected details of an embodiment of a deep learningaccelerator as Deep Learning Accelerator 400. Each of PE 499 elementshas couplings to other of PE 499 elements. Two of the PE elements (PE497 and PE 498) are illustrated with unique identifiers, and areotherwise respectively identical to instances of PE 499. PE 497 isillustrated with identifiers for each of four couplings (North coupling430, East coupling 431 with PE 498, and South coupling 432) to others ofthe PEs and one of the I/O FPGAs (West coupling 433), but is otherwiseidentical to others of the PE elements illustrated. In some embodimentsand/or usage scenarios, the couplings are logical and/or physical. Invarious embodiments and/or usage scenarios, the couplings are usable tocommunicate wavelets, backpressure information, or both. In variousembodiments and/or usage scenarios, all or any portions of the physicalcouplings are to physically adjacent PEs. In some embodiments and/orusage scenarios, the PEs are physically implemented in a 2D grid. Insome embodiments and/or usage scenarios, the PEs are physicallyimplemented in a 2D grid of aligned rectangles, and physically adjacentPEs correspond to PEs sharing a horizontal boundary (North/South PEswith respect to each other) and PEs sharing a vertical boundary(East/West PEs with respect to each other).

In some embodiments and/or usage scenarios, an array of identicalinstances of a same ASIC is formed on a wafer, and each of the sameASICs comprises a plurality of identical instances of a same PE (e.g.,PE 499), forming a wafer (e.g., Wafer 412) usable in wafer-scaleintegration techniques. Unless indicated to the contrary, referencesherein to a “wafer” (including to Wafer 412) are applicable toembodiments of a whole or substantially whole wafer as well as toembodiments of a significant portion of a wafer. In some embodimentsand/or usage scenarios, one or more peripheral portions of the PEs arecoupled to I/O FPGAs 420. Example ASICs are illustrated as ASIC 410,comprising a column-organized section of PEs (replicated, e.g., in aone-dimensional fashion to form a wafer), and ASIC 411, comprising asquare-organized section or a rectangular-organized section of PEs(replicated, e.g., in a two-dimensional fashion to form a wafer). Otherorganizations of ASICs on a wafer are contemplated.

In some embodiments and/or usage scenarios, neurons associated withlayers in a neural network are generally placed on PE 499 elements in aleft to right fashion, with earlier layers (e.g., the input layer) onthe left and subsequent layers (e.g., the output layer) on the right.Accordingly, data flow during training is illustrated conceptually asdashed-arrows Forward 401, Delta 402, and Chain 403. During Forward 401,stimuli are applied to the input layer and activations from the inputlayer flow to subsequent layers, eventually reaching the output layerand producing a forward result. During Delta 402, deltas (e.g.,differences between the forward result and the training output data) arepropagated in the backward direction. During Chain 403, gradients arecalculated based on the deltas (e.g., with respect to the weights in theneurons) as they are generated during Delta 402. In some embodimentsand/or usage scenarios, processing for Delta 402 is substantiallyoverlapped with processing for 403.

In some embodiments and/or usage scenarios, Deep Learning Accelerator400 is an implementation of Deep Learning Accelerator 120 of FIG. 1. Insome embodiments and/or usage scenarios, individual PE 499 elementscorrespond to individual PEs of PEs 122 of FIG. 1. In some embodimentsand/or usage scenarios, each ASIC 410 element or alternatively each ASIC411 element corresponds to all or any portions of PEs of PEs 122implemented as individual integrated circuits. In some embodimentsand/or usage scenarios, each ASIC 410 element or alternatively each ASIC411 element corresponds to (optionally identical) portions of PEs 122implemented via respective dice of a wafer. In some embodiments and/orusage scenarios, I/O FPGAs 420 elements collectively correspond to FPGAs121 of FIG. 1.

In some embodiments and/or usage scenarios, the placement of neurons(e.g., associated with layers in a neural network) onto PE 499 elementsis performed in whole or in part by all or any portions of PlacementServer(s) SW 210 of FIG. 2.

Fabric Overview

As illustrated, e.g., in FIG. 4, an embodiment of a deep learningaccelerator comprises a plurality of PEs coupled to each other via afabric. Each PE includes a CE (e.g., for performing computations) and arouter (e.g., for managing and/or implementing movement of informationon the fabric).

The fabric operates as a communication interconnect between all the PEsin the deep learning accelerator. The fabric transfers wavelets, e.g.,via 30-bit physical couplings to enable transfer of an entire waveletper cycle (e.g., core clock cycle). Conceptually the fabric is a localinterconnect distributed throughput the PEs such that each PE is enabledto communicate directly with its (physical) neighbors. Communication toother-than (physical) neighbors is via hops through intermediate nodes,e.g., others of the PEs. In some embodiments and/or usage scenarios, adistributed local fabric topology efficiently maps to a neural networkworkload, e.g., each layer sends data to a neighboring layer) and/or isimplementable with relatively lower cost in hardware.

An example fabric comprises 16 logically independent networks referredto as colors. Each color is a virtual network, e.g., virtual channel,overlaid on a single physical network. Each color has dedicated physicalbuffering resources but shares the same physical routing resources. Thededicated physical buffers enable non-blocking operation of the colors.The shared physical routing reduces physical resources. In variousembodiments and/or usage scenarios, a fabric comprises various numbersof colors (e.g., 8, 24, or 32).

There is a routing pattern associated with each color and implemented bythe routers. The routing pattern of each pattern is programmable and insome embodiments is statically configured, e.g., based at least in parton determinations made by Placement Server(s) SW 210 and/or Neuron to PEMapping SW 212 of FIG. 2. Once configured, e.g., under control ofsoftware (such as Connection Server(s) SW 220 of FIG. 2), each color isa fixed routing pattern. All data that flows within a color always flowsin accordance with the fixed routing pattern. There are no dynamicrouting decisions. The fixed routing matches neural networkcommunication patterns where neuron connections are staticallyspecified. The fixed routing enables relatively lower cost hardwareimplementation.

As illustrated in FIG. 4, an example (physical) fabric topologycomprises a 2D mesh with each hop in the X or Y dimension (e.g. West 511or North 513 of FIG. 5, respectively) performed in a single core clockcycle. In addition to the 2D mesh illustrated, some embodiments furthercomprise “skip” connections, e.g., in the horizontal dimension and“loop” connections, e.g., in the vertical dimension. An example skipconnection enables PEs in a same row of the 2D mesh and physicallyseparated by N other PEs to communicate with each other as if the PEswere physically adjacent. A hop along a skip connection (e.g. Skip West512 of FIG. 5) is performed in a single core clock cycle. In variousembodiments, an example loop connection enables a PE at the bottom of acolumn of PEs to communicate with a PE at the top of the column as ifthe PEs were physically adjacent. In some embodiments, a hop along aloop connection is performed in a single core clock cycle.

Performing each hop in the X or Y dimension in a single clock, in someembodiments and/or usage scenarios, enables simplifying implementationof arbitrary programmable routing topologies and related timingconstraints. In some circumstances, the single cycle per hop latency iscompatible with an associated pipelined data flow pattern. In somecircumstances (e.g., when communicating from one layer to a next layer),the single cycle per hop latency adds additional latency and reducesperformance. The additional latency is worst when the layer is deep anduses many PEs, since more hops are used to escape the layer and to reachall the PEs of the next layer. The additional latency results in overallworkload pipeline length increasing and therefore storage (e.g. forforward pass activations) increasing.

The skip connections are used to reduce the additional latency. Consideran example. Each skip connection skips 50 PEs in a single core clockcycle. The latency to enter the first skip connection is 49 hopsmaximum. The latency to reach a final PE after exiting a final skipconnection is 49 hops maximum. Therefore, there is a 98-core clock cyclemaximum latency overhead and a 49-core clock cycle average latencyoverhead. The latency to process a layer is 2000 core clock cycles.Thus, in the example, there is a 5% maximum overall overhead and a 2.5%average overall overhead.

In some embodiments and/or usage scenarios, each row has skipconnections and each column has loop connections. In some embodimentsand/or usage scenarios, each skip connection skips 50 PEs, and eachcolumn has 200 PEs that a loop connection encompasses. In someembodiments, a single loop connection (e.g., in a context of a column ofPEs, between the PE at the bottom of the column and the PE at the top ofthe column) approximately physically spans the column, and in otherembodiments, loop connections of the column are physically implementedby folding so that the average and worst case loop hops approximatelyphysically span two PEs.

In some embodiments and/or usage scenarios, the fabric interconnects200×100 PEs per ASIC, with 200 PEs in the vertical dimension and 100 PEsin the horizontal dimension. The fabric is general purpose and usable bysoftware executing on the PEs (e.g. Task SW on PEs 260 of FIG. 2) forany function. In some embodiments and/or usage scenarios, the softwareuses the horizontal dimension for communicating data between layers(e.g., activation broadcasting). The communicating data between layersis optionally and/or selectively via one or more skip connections. Insome embodiments and/or usage scenarios, the software uses the verticaldimension for communicating data within a layer (e.g., partial sumaccumulating). The communicating within a layer is optionally and/orselectively via one or more loop connections. In some circumstances,partial sum accumulating is via a ring topology.

Conceptually, on the fabric, backpressure information flows along thesame topology and at the same rate as data the backpressure informationcorresponds to, but in the opposite direction of the corresponding data.E.g., a router sends backpressure information along the reverse path ofthe fixed routing pattern. There is an independent backpressure channel(e.g., signal) for each color, enabling communicating backpressureinformation for multiple colors simultaneously. The independent backpressure channels simplify, in some embodiments and/or usage scenarios,the backpressure communication when there are multiple queues drainingon the same cycle (e.g., to different outputs).

When a color is back pressured, data queued at each hop within thefabric is stalled. Conceptually, the queued data is an extension to aqueue at the destination since it is drained into the destination oncethe backpressure is released. For example, the backpressure signal froma particular PE and corresponding to a particular color is only assertedwhen a data queue of the router of the particular PE and correspondingto the particular color is at a predetermined threshold (e.g., full ornearly full). Therefore, with respect to the particular color, dataflows until reaching a stalled PE, such that the data queue effectivelyoperates as a portion of a distributed in-fabric queue.

The fixed routing pattern provides for multicast replication within eachrouter. Multicast enables high fan-out communication patterns, such aswithin some neural network workloads. To perform multicast, each routernode is statically configured with multiple outputs per multicast color.The router replicates an incoming wavelet corresponding to the multicastcolor to all outputs specified by the static configuration beforeprocessing the next wavelet of the multicast color. In somecircumstances, there is a plurality of multicast colors, each staticallyconfigured with a respective set of multiple outputs.

The router provides for multiple input sources per color and processes asingle active input source at a time. Coordination of the input sourcesis performed, for example, by software at a higher-level (e.g. flowcontrol dependency, explicit messaging between PEs, or other suitablemechanisms) so that only a single input source is active at a time.Implementing a single active input source enables, in some embodimentsand/or usage scenarios, relatively lower-cost hardware since the routerhas a single buffer per color instead of a buffer per input source.

Since there is only a single active input source at a time, there is notany congestion within a color. However, in some circumstances,congestion occurs between colors since the colors share a singlephysical channel. The router responds to the congestion by schedulingbetween ready colors onto a single shared output channel.

Deadlock on the fabric is possible since the fabric is blocking (e.g.,the fabric and the routers have no hardware deadlock avoidancemechanisms). Deadlock is avoided by software configuring the fixedrouting patterns to be free of dependent loops, thus avoiding circulardependencies and deadlock.

Software also ensures there are no circular dependencies through PE datapath resources. Such dependencies would otherwise be possible since thetraining workload shares the same physical PE data path for all threemega-phases (forward pass, delta pass, and chain pass) and processing ofthe delta pass and the chain pass is on the same PEs as processing ofthe forward pass. To break any circular dependencies, software ensuresthat all tasks in the (forward pass, delta pass, and chain pass) loop donot block indefinitely. To do so, at least one task in the loop isguaranteed to complete once scheduled. The task scheduling is enabled bythe wavelet picker in the compute element. The picker is programmed toschedule a wavelet only when the downstream color for the wavelet isavailable. It is also independently desirable for software to programtasks with the foregoing property for performance, in some embodimentsand/or usage scenarios.

In the event of incorrect configuration leading to deadlock, there is awatchdog mechanism that detects lack of progress and signals a fault tomanagement software.

Processing Element: Compute Element and Router

FIG. 5 illustrates selected details of an embodiment of a PE as PE 500of a deep learning accelerator. PE 500 comprises Router 510 and ComputeElement 520. Router 510 selectively and/or conditionally communicates(e.g. transmits and receives) wavelets between other PEs (e.g.,logically adjacent and/or physically adjacent PEs) and PE 500 viacouplings 511-516. Couplings 511-516 are illustrated as bidirectionalarrows to emphasize the bidirectional communication of wavelets on thecouplings. Backpressure information is also transmitted on the couplingsin the reverse direction of wavelet information the backpressurecorresponds to. Router 510 selectively and/or conditionally communicateswavelets to PE 500 (e.g., Compute Element 520) via Off Ramp 521 andcommunicates wavelets from PE 500 (e.g., Compute Element 520) via OnRamp 522. Off Ramp 521 is illustrated as a unidirectional arrow toemphasize the unidirectional communication of wavelets on the coupling(e.g., from Router 510 to Compute Element 520). Backpressure informationis also transmitted on the coupling in the reverse direction of waveletinformation (e.g. from Compute Element 520 to Router 510). On Ramp 522is illustrated as a unidirectional arrow to emphasize the unidirectionalcommunication of wavelets on the coupling (e.g., from Compute Element520 to Router 510). Backpressure information is also transmitted on thecoupling in the reverse direction of wavelet information (e.g. fromRouter 510 to Compute Element 520).

Compute Element 520 performs computations on data embodied in thewavelets according to instruction address information derivable from thewavelets. The instruction address information is used to identifystarting addresses of tasks embodied as instructions stored in storage(e.g., any one or more of memory, cache, and register file(s)) of thecompute element. Results of the computations are selectively and/orconditionally stored in the storage and/or provided as data embodied inwavelets communicated to the router for, e.g., transmission to the otherPEs and or PE 500.

In addition to data, Router 510 selectively and/or conditionallycommunicates (e.g. transmits and receives) backpressure informationbetween the other PEs and PE 500 via couplings 511-516. Router 510selectively and/or conditionally transmits backpressure information toPE 500 via On Ramp 522. Router 510 receives backpressure informationfrom PE 500 via Off Ramp 521. The backpressure information provided tothe other PEs, as well as the backpressure information provided to PE500, is used by the other PEs and PE 500 to stall transmitting data(e.g. wavelets) that would otherwise be lost due to insufficient queuespace to store the data in Router 510. The backpressure informationreceived from the other PEs and PE 500 is used respectively by Router510 to prevent transmitting data (e.g. wavelets) that would otherwise belost due respectively to insufficient queue space in the routers of theother PEs and insufficient space in input queues of Compute Element 520.

In various embodiments, any one or more of 511-516 are omitted.

In some embodiments and/or usage scenarios, PE 500 is an embodiment ofPE 499 of FIG. 4, and/or elements of PE 500 correspond to animplementation of PE 499. In some embodiments and/or usage scenarios,North 513, East 515, South 516, and West 511 correspond respectively toNorth coupling 430, East coupling 431, South coupling 432, and Westcoupling 433 of FIG. 4.

FIG. 6 illustrates selected details of an embodiment a router of a PE,as Router 600. Consider that there is a plurality of PEs, eachcomprising a respective router and a respective CE. Router 600 is aninstance of one of the respective routers. Router 600 routes wavelets,in accordance with color information of the wavelets and routingconfiguration information, to the CE of the PE that the instant routeris comprised in, as well as others of the routers. The routed waveletsare variously received by the instant router and/or generated by the CEof the PE that the instant router is comprised in. The routing enablescommunication between the PEs. Stall information is communicated toprevent overflowing of wavelet storage resources in Router 600.

Router 600 comprises four groups of interfaces, Data In 610, Data Out620, Stall Out 630, and Stall In 640. Data In 610, Data Out 620, StallOut 630, and Stall In 640 respectively comprise interface elements611-617, 621-627, 631-637, and 641-647. Router 600 further comprisesWrite Dec 651, Out 652, Gen Stall 656, and Stall 657, respectivelycoupled to Data In 610, Data Out 620, Stall Out 630, and Stall In 640.Router 600 further comprises Sources 653 comprising Src 670 coupled toGen Stall 656. Router 600 further comprises Data Queues 650, ControlInfo 660, and Router Sched 654. Control Info 660 comprises Dest 661 andSent 662.

Conceptually, skipX+ 611, skipX+ 621, skipX+ 631, and skipX+ 641comprise one of seven ‘directions’, e.g., the skipX+′ direction. In someembodiments, the skipX+ direction corresponds to Skip East 514 of FIG.5. SkipX− 612, SkipX− 622, SkipX− 632, and SkipX− 642 comprise a second,‘SkipX−’ direction. In some embodiments, the skipX− directioncorresponds to Skip West 512 of FIG. 5. X+ 613, X+ 623, X+ 633, and X+643 comprise a third, ‘X+’ direction. In some embodiments, theX+direction corresponds to East 515 of FIG. 5. X− 614, X− 624, X− 634,and X− 644 comprise a fourth, ‘X−’ direction. In some embodiments, theX− direction corresponds to West 511 of FIG. 5. Y+ 615, Y+ 625, Y+ 635,and Y+ 645 comprise a fifth, ‘Y+’ direction. In some embodiments, the Y+direction corresponds to North 513 of FIG. 5. Y− 616, Y− 626, Y− 636,and Y− 646 comprise a sixth, ‘Y−’ direction. In some embodiments, the Y−direction corresponds to South 516 of FIG. 5. Lastly, On Ramp 617, OffRamp 627, On Ramp 637, and Off Ramp 647 comprise a seventh, ‘On/OffRamp’ direction. In some embodiments, On Ramp 617 and On Ramp 637portions of the On/Off Ramp direction correspond to On Ramp 522 of FIG.5. In some embodiments, Off Ramp 627 and Off Ramp 647 of the On/Off Rampdirection correspond to Off Ramp 521 of FIG. 5.

Data In 610 is for receiving up to one wavelet from each direction eachcore clock cycle. Stall Out 630 is for transmitting stall information ineach direction for each color each core clock cycle. Data Out 620 is fortransmitting up to one wavelet to each direction in each core clockcycle. Stall In 640 is for receiving stall information from eachdirection for each color each core clock cycle.

Data Queues 650 is coupled to Write Dec 651 to receive incoming waveletinformation and coupled to Out 652 to provide outgoing waveletinformation. Data Queues 650 is further coupled to Gen Stall 656 toprovide data queue validity information (e.g., corresponding tofullness) used for, e.g., generating stall information. Router Sched 654is coupled to Control Info 660 to receive control information relevantto scheduling queued wavelets. Router Sched 654 is further coupled toStall 657 to receive stall information relevant to scheduling queuedwavelets. Router Sched 654 is further coupled to Out 652 to directpresentation of queued wavelets on one or more of 621-627. Router Sched654 is further coupled to Gen Stall 656 to partially direct generationof stall information.

In some embodiments, Data Queues 650 comprises two entries per color (c0. . . c15). Each entry is enabled to store at least payload informationof a wavelet. In various embodiments, color information of the waveletis not stored. A first of the entries is used to decouple the input ofthe queue from the output of the queue. A second of the entries is usedto capture inflight data when a stall is sent in parallel (e.g., on asame core clock cycle) with the inflight data. In various embodiments,Data Queues 650 comprises a number of bits of storage equal to a numberof colors multiplied by a number of bits of stored information perwavelet multiplied by a number of queue entries per color, e.g., 864bits=16 colors*27 bits of wavelet data*2 entries per color.Alternatively, 33 bits of wavelet data are stored, and Data Queues 650comprises 1056 bits=16 colors*33 bits of wavelet data*2 entries percolor. In various embodiments, Data Queues 650 is implemented via one ormore registers and/or a register file. Write Dec 651 stores, for each ofthe directions, information of the respective incoming wavelet into anentry of Data Queues 650 corresponding to the color of the incomingwavelet.

In some embodiments, Router Sched 654 comprises a scheduler for each ofthe directions (e.g., per 621-627). For each direction, the respectivescheduler assigns available data in Data Queues 650 to the respectivedirection. Destination information per color is (statically) provided byDest 661. In various embodiments, Dest 661 comprises a number of bits ofstorage equal to a number of colors multiplied by a number ofdirections, e.g., 112 bits=16 colors*7 directions. In variousembodiments, Dest 661 is implemented via one or more registers and/or aregister file. In some embodiments, Dest 661 comprises a data structureaccessed by color that provides one or more directions as a result.E.g., a register file/array addressed by color encoded as a binary valueand providing one bit per direction as a bit vector, each asserted bitof the bit vector indicating the color is to be sent to the associateddirection(s).

Each of the schedulers operates independently of one another. Thus, formulticast outputs, a single wavelet is selectively and/or conditionallyscheduled onto different directions in different core clock cycles, oralternatively in a same core clock cycle. Sent 662 is used to trackwhich direction(s) a wavelet has been sent to. Each scheduler picks acolor if the color has not been previously sent and the direction is notstalled for the color. In various embodiments, Sent 662 comprises anumber of bits of storage equal to a number of colors multiplied by anumber of directions, e.g., 112 bits=16 colors*7 directions. In variousembodiments, Sent 662 is implemented via one or more registers and/or aregister file.

In various embodiments, each scheduler implements one or more schedulingpolicies, e.g., round-robin and priority. The round-robin schedulingpolicy comprises the scheduler choosing between all available colors oneat a time, conceptually cycling through all the colors before picking asame color again. The priority scheduling policy comprises the schedulerchoosing from among a first set of predetermined colors (e.g., colors0-7) with higher priority than from among a second set of predeterminedcolors (e.g., colors 8-15).

In some embodiments, Stall 657 is enabled to capture stall informationand comprises a number of bits of storage equal to a number of colorsmultiplied by a number of directions, e.g., 112 bits=16 colors*7directions. In various embodiments, Stall 657 is implemented via one ormore registers and/or a register file.

In some embodiments, stall information is generated by Gen Stall 656 forall the colors of all the directions, based on occupancy of Data Queues650. E.g., there is a stall generator for each color of each of 631-637.Src 670 stores and provides to Gen Stall 656 information to map acorresponding color of Data Queues 650 to one or more correspondingdirections. In response to insufficient queue space in Data Queues 650corresponding to a particular color, the directions acting as sourcesfor the particular color are directed to stall providing further input,until queue space becomes available in Data Queues 650 for the furtherinput. In various embodiments, Src 670 comprises a number of bits ofstorage equal to a number of colors multiplied by a number ofdirections, e.g., 112 bits=16 colors*7 directions. In variousembodiments, Src 670 is implemented via one or more registers and/or aregister file. In some embodiments, Src 670 comprises a data structureaccessed by color that provides one or more directions as a result.E.g., a register file/array addressed by color encoded as a binary valueand providing one bit per direction as a bit vector, each asserted bitof the bit vector indicating the color is sourced from the associateddirection(s).

In various embodiments and/or usage scenarios, all or any portions ofinformation retained in any one or more of Src 670 and Dest 661corresponds to all or any portions of routing configuration information.In various embodiments and/or usage scenarios, all or any portions ofthe routing configuration information is determined, e.g., based atleast in part on Placement Server(s) SW 210 and/or Neuron to PE MappingSW 212 of FIG. 2. In various embodiments and/or usage scenarios, therouting configuration information is distributed to routers, e.g., undercontrol of software (such as Connection Server(s) SW 220, Misc SW onFPGAs 250, and/or Task SW on PEs 260 of FIG. 2). In various embodimentsand/or usage scenarios, one or more predetermined colors (e.g. colorzero) are used to distribute, in accordance with a predetermined fixedrouting pattern, all or any portions of the routing configurationinformation and/or all or any portions of compute element configurationinformation. An example of the predetermined fixed routing pattern is apredetermined multicast topology, optionally and/or conditionally inconjunction with a non-stalling flow. In some embodiments and/or usagescenarios, the distribution of the configuration information isimplemented via a wavelet format unique to the distribution. Wavelets ofthe unique format are parsed and interpreted, e.g., by a hard-codedstate machine monitoring Off Ramp 627.

In various embodiments, each of interface elements 611-616, 621-626,631-636, and 641-646 is variously implemented via passive interconnect(e.g., wire(s) without buffering), active interconnect (e.g., wire(s)with selective and/or optional buffering), and coupling with logic toaccommodate additional functionality between one instance of Router 600and another instance of Router 600. In various embodiments, each ofinterface elements 617, 627, 637, and 647 is variously implemented viapassive interconnect (e.g., wire(s) without buffering), activeinterconnect (e.g., wire(s) with selective and/or optional buffering),and coupling with logic to accommodate additional functionality betweenthe instant router and the CE of the PE the instant router is comprisedin.

In some embodiments and/or usage scenarios, Router 600 is animplementation of Router 510 of FIG. 5.

FIG. 7A illustrates selected details of an embodiment of processingassociated with a router of a processing element, as Wavelet Ingress710. Conceptually, the router accepts as many wavelets as possible fromingress ports, queuing as necessary and as queue space is available, androutes as many wavelets as possible to egress ports per unit time (e.g.,core clock cycle). In some embodiments and/or usage scenarios, there isone queue per color.

Wavelet Ingress 710 comprises actions 711-713 corresponding to waveletingress from (logically and/or physically) adjacent PEs and/or aninstant PE, for each respective router direction (e.g., any of 611-617of FIG. 6). The router waits for an incoming wavelet (Wait for Wavelet711). In response to the incoming wavelet, the wavelet is received(Receive Wavelet 712) and written into a router queue corresponding to acolor comprised in the wavelet (Wavelet=>Router Q 713). In someembodiments, the writing is at least partly under the control of WriteDec 651. Flow then returns to wait for another wavelet. In someembodiments and/or usage scenarios, a respective instance of WaveletIngress 710 operates concurrently for each router direction. In variousembodiments and/or usage scenarios, any one or more of all or anyportions of actions of 710 correspond to actions performed by and/orrelated to all or any portions of any one or more elements of Router 600of FIG. 6.

FIG. 7B illustrates selected details of an embodiment of generating andproviding backpressure information associated with a compute element ofa processing element as flow 740. Actions of flow 740 are performed byvarious agents. A PE comprises a CE that performs actions 744-746, asillustrated by CE of PE 741. The PE further comprises a router thatperforms action 747, as illustrated by Router of PE 742.

In some embodiments, flow for generating and transmitting backpressureinformation begins (Start 743) by determining which input queues of theCE are storing more wavelets than a per-queue threshold (Determine InputQ(s) Over Threshold 744). In some embodiments, the per-queue thresholdis predetermined. In various embodiments, the threshold for an inputqueue is two less than the maximum capacity of the input queue (e.g., aninput queue enabled to store six wavelets has a threshold of four). Insome other embodiments, the threshold for an input queue is one lessthan the maximum capacity. The determining occurs every period, e.g.,every core clock cycle, and considers wavelets received and stored inthe input queues and wavelets consumed and removed from the input queuesin the period. Colors associated with each input queue and aredetermined by the CE (Determine Colors Associated with Input Q(s) 745).In some embodiments, an input queue is associated with multiple colors,and in other embodiments an input queue is associated with a singlecolor. Based on whether the associated input queue is over/under thethreshold, a stall/ready state is determined by the CE for each of thecolors and provided as signals by the CE to the router (ProvideStall/Ready to Router 746).

In various embodiments, a ready state for a color indicates that theassociated input queue has sufficient capacity to receive a number ofwavelets (e.g., one or two) and the stall state indicates that theassociated input queue does not have sufficient capacity to receive thenumber of wavelets. Based upon the provided stall/ready states, Routerof PE 742 conditionally provides a wavelet to the CE (Provide Wavelet toCE in Accordance with Stall/Ready 747) and flow concludes (End 748). Insome embodiments and/or usage scenarios, the router provides a waveletfor a color in the ready state and does not provide a wavelet for acolor in the stall state.

In various embodiments and/or usage scenarios, actions of flow 740 areconceptually related to a CE, e.g., CE 800 of FIG. 8 and a router, e.g.,Router 600 of FIG. 6. In some embodiments, the input queues correspondto Input Qs 897. In various embodiments, the colors associated with eachinput queue are determined by computing the inverse of Hash 822. In someembodiments, the group of stall/ready signals is provided to the routervia Off Ramp 647. In some embodiments and/or usage scenarios, one ormore of: any portion or all of FIG. 9A, any portion or all of FIG. 16,and portions of FIG. 23 (e.g., Read (Next) Source Data Element(s) fromQueue/Memory 2310) correspond to portions of consuming a wavelet from aninput queue. In various embodiments, portions of FIG. 15 (e.g., WriteWavelet to Picker Queue 1507) correspond to receiving and storing awavelet in an input queue.

FIG. 7C illustrates selected details of an embodiment of generating andproviding backpressure information associated with a router of aprocessing element, as flow 750. Actions of flow 750 are performed byvarious agents. A router of a PE performs actions 756-759, asillustrated by Router of PE 751. The PE further comprises a CE thatperforms action 760, as illustrated by CE of PE 752. One or more routersof neighboring PEs perform actions 761 as illustrated by Router(s) ofNeighbor(s) 753.

In some embodiments, flow for generating and providing backpressureinformation begins (Start 755) by the router of the PE determining whichdata queues of the router are storing more wavelets than a threshold(Determine Data Queue(s) Over Threshold 756). In some embodiments, thethreshold is predetermined. In various embodiments, the threshold for adata queue is one less than the maximum capacity of the queue (e.g., aqueue enabled to store two wavelets has a threshold of one). Thedetermining occurs every period, e.g., every core clock cycle, andconsiders wavelets received and stored in the data queues and waveletsthat are transmitted and removed from the data queues in the period. Therouter determines sources of wavelets for each color (Check ColorSources 757). Based on whether the data queues are over/under thethreshold and the sources of wavelets, for each router output (e.g., thelocal CE and neighbor PEs), the router determines which colors are in astall/ready state (Determine Stall/Ready Colors for CE, Neighbors 758).

In various embodiments, a ready state for a color indicates that theassociated data queue for the color has sufficient capacity to receive anumber of wavelets (e.g., one or two) and the stall state indicates thatthe associated data queue does not have sufficient capacity to receivethe number of wavelets. For each output, the stall/ready state for thecolors are provided as a group by asserting stall/ready signals to CE ofPE 752 and to Router(s) of Neighbor(s) 753 (Provide Stall/Ready to CE,Neighbors 759). In some embodiments and/or usage scenarios, backpressureinformation provided to CE of PE 752 and each router of Router(s) ofNeighbor(s) 753 is identical. Based upon the provided stall/readystates, CE of PE 752 conditionally provides a wavelet to Router of PE751 (Provide Wavelet to Router in Accordance with Stall/Ready 760),Router(s) of Neighbor(s) 753 conditionally provide wavelet(s) to Routerof PE 751 (Provide Wavelet to Router in Accordance with Stall/Ready761), and flow concludes (End 762). In some embodiments and/or usagescenarios, the CE and neighbor routers provide a wavelet for a color inthe ready state and do not provide a wavelet for a color in the stallstate.

In various embodiments and/or usage scenarios, actions of flow 750 areconceptually related to a CE, e.g., CE 800 of FIG. 8 and a router, e.g.,Router 600 of FIG. 6. In some embodiments, the router receivesstall/ready colors via Stall In 640 (e.g., from a local CE via Off Ramp647 and from neighbor PEs via 641-646). In various embodiments, eachcolor and associated source(s) are stored in Src 670, which indicatesdirection(s) to provide stall/ready signals to for each respectivecolor. For example, the entry for color seven in Src 670 indicates thatthe sources include the local CE (On Ramp 617) and X+ 613; thus,stall/ready state for color seven is provided to the local CE and X+. Insome embodiments, a group of stall/ready signals is transmitted from therouter to the CE via On Ramp 637. In various embodiments, a group ofstall/ready signals is provided from the router to the routers ofneighbor PEs via 631-636 of Stall Out 630.

FIG. 7D illustrates selected details of an embodiment of stallingprocessing associated with a compute element of a processing element, asflow 780. Actions of flow 780 are performed by a CE of a PE, asillustrated by CE of PE 781.

In some embodiments, flow for stalling processing begins (Start 782) bythe CE determining whether any output queues are storing a per-queuemaximum capacity of wavelets (Determine Full Output Q(s) 783). In someembodiments, the per-queue maximum capacity is predetermined. Thedetermining occurs every period, e.g., every core clock cycle, andconsiders wavelets that are created and stored in the output queues andwavelets that are transmitted to the router and removed from the outputqueues in the period. In response to determining an output queue isstoring the maximum capacity of wavelets, the CE determines the colorsassociated with the output queue (Determine Colors Associated with FullOutput Q(s) 784) and stalls processing for those colors (StallProcessing for Colors Associated with Full Output Q(s) 785), concludingflow (End 786).

In various embodiments and/or usage scenarios, actions of flow 780 areconceptually related to a CE, e.g., CE 800 of FIG. 8. In someembodiments, the output queues correspond to Output Queues 859. Invarious embodiments and usage scenarios, wavelets are stored in outputqueues in response to receiving a stall from the router on the colorassociated with the wavelet. In some embodiments and usage scenarios,each of Output Queues 859 is associated with one or more colors and theassociation is tracked in a portion of Output Queues 859. In otherembodiments, each of Output Queues 859 is associated with a singlecolor. In some embodiments and usage scenarios, the CE stalls processingassociated with colors associated with output queues storing the maximumcapacity of wavelets. In some embodiments, action 785 is performed atleast in part by Picker 830. In various embodiments, processing isenabled for any colors associated with output queues storing less thanthe maximum capacity of wavelets.

FIG. 8 illustrates selected details of an embodiment of a computeelement of a processing element, as CE 800.

In various embodiments, CE 800 is coupled to Router 600 of FIG. 6. Forexample, Off Ramp 820, On Ramp 860, Off Ramp 847, and On Ramp 837 arecoupled respectively to Off Ramp 627, On Ramp 617, On Ramp 647, and OnRamp 637. CE 800 comprises Qdistr 824 coupled to receive wavelets viaOff Ramp 820. Qdistr 824 is coupled to transmit wavelets to SchedulingInfo 896. Scheduling Info 896 comprises Input Qs 897, Active Bits 898,and Block Bits 899. Scheduling Info 896 is coupled to Off Ramp 847 tosend stall information (e.g., stall/ready signals for each color) to arouter.

In various embodiments, Input Qs 897 comprises a virtual queue for eachfabric color and each local color. The virtual queues for each fabriccolor are usable, e.g., to hold wavelets created by other processingelements and associated with the respective color. The virtual queuesfor each local color are usable, e.g., to hold wavelets created by CE800 and associated with the respective color. In various embodiments,the virtual queues are implemented by one or more physical input queues.In some other embodiments, Input Qs 897 comprises a physical queue foreach fabric color and each local color. Each one of Input Qs 897 (e.g.,Input Q0 897.0) is associated with a respective one of Active Bit 898(e.g., Active Bit 0 898.0) and Block Bits 899 (e.g., Block Bit 0 899.0).Each one of Active Bits 898 and each one of Block Bits 899 containinformation about the respective one of Input Qs 897, e.g., Block Bit N899.N indicates whether Input QN 897.N is blocked.

In various embodiments, there is variously a physical Q for each color,one or more physical Qs for a predetermined subset of colors, and one ormore physical Qs for a dynamically determined subset of colors. Invarious embodiments, there is variously one or more physical Qs of asame size (e.g., each enabled to hold a same number of wavelets) and oneor more physical Qs of differing sizes (e.g., each enabled to hold adifferent number of wavelets). In various embodiments, there are one ormore physical Qs that are variously mapped to virtual Qs, each of thevirtual Qs being associated with one or more colors. For example, thereare N logical Qs and less than N physical Qs. For another example, someof Input Qs 897 are enabled to hold eight wavelets and others of InputQs 897 are enabled to hold three wavelets. In some embodiments, trafficfor one or more colors associated with a particular one of Input Qs 897is estimated and/or measured, and the particular one of Input Qs 897 isenabled to hold a particular number of wavelets based on the traffic. Insome embodiments, one or more of the physical Qs are implemented by oneor more of: registers and SRAM.

Hash 822 is coupled to Qdistr 824 and selects a physical queue to storea wavelet, based at least in part on the color of the wavelet (e.g., byapplying a hash function to the color). In some embodiments, the colorassociated with a wavelet payload is stored explicitly with the waveletpayload in a queue, such that an entry in the queue holds an entirewavelet (payload with color). In some embodiments, the color associatedwith a wavelet payload is not stored explicitly with the wavelet payloadin a queue, such that an entry in the queue stores a wavelet payloadwithout storing an associated color. The color of the wavelet payload isinferred, such as from the specific queue the wavelet payload is storedin.

In some embodiments, one or more of Active Bits 898 and Block Bits 899are implemented as respective bit vectors with N entries, one entry foreach color. In various embodiments, one or more of Active Bits 898 andBlock Bits 899 are implemented as respective bit fields in a tablecomprising one entry for each color.

Picker 830 is coupled to Scheduling Info 896, RF 842, Dec 840, Base 890,PC 834, I-Seq 836, and D-Seq 844. RF, Dec, Base, PC, I-Seq, and D-Seqare respectively shorthand for Register File, Decoder, Base Register,Program Counter, Instruction Sequencer, and Data Sequencer. Picker 830is enabled to select a wavelet for processing from one of Input Qs 897.In some embodiments, Picker 830 selects a wavelet by selecting one ofInput Qs 897, and selecting the oldest wavelet in the selected queue. Insome scenarios, Picker 830 selects a new wavelet for processing when Dec840 signals that a terminate instruction has been decoded. In some otherscenarios (e.g., an instruction accessing fabric input), Picker 830selects a new wavelet for processing from one of Input Qs 897 inresponse to a queue identifier received from D-Seq 844.

Picker 830 receives the selected wavelet from one of Input Qs 897 and isenabled to selectively and/or optionally send one or more of data andindex from the selected wavelet to RF 842. In some embodiments, Input Qs897 is coupled to Data Path 852, and the Data Path is enabled to receivedata directly from one of the Qs. Picker 830 is enabled to read a baseaddress from Base 890 and calculate an instruction address to send to PC834 and I-Seq 836. Base 890 stores a base address and is also coupled toD-Seq 844. PC 834 stores the address of the next instruction to fetch.In various embodiments, Base 890 and PC 834 are implemented asregisters. In some embodiments, D-Seq 844 is enabled to read a baseaddress from Base 890 and request data at one or more addresses fromMemory 854 and D-Store 848, based at least in part upon the value readfrom Base 890.

Picker 830 is further enabled to select an activated color (as indicatedby assertion of a corresponding one of Active Bits 898) for processinginstead of selecting a wavelet for processing. A task corresponding tothe selected color is initiated. In some embodiments and/or usagescenarios, unlike selection of a wavelet for processing, no informationis provided to RF 842, and thus data communicated to the initiated taskis via, e.g., global registers and/or memory.

I-Seq 836 is coupled to PC 834 and is enabled to read and modify PC 834(e.g., increment for a sequential instruction or non-sequentially for abranch instruction). I-Seq 836 is also coupled to Memory 854 and isenabled to provide an instruction fetch address to Memory 854 (e.g.,based upon PC 834).

Memory 854 is further coupled to Dec 840, Data Path 852, and D-Seq 844.In response to an instruction fetch address from I-Seq 836, Memory 854is enabled to provide instructions located at the instruction fetchaddress to Dec 840 (an instruction decoder). In various embodiments,Memory 854 is enabled to provide up to three instructions in response toeach instruction fetch address. In some embodiments, an instruction isformatted in accordance with one or more of FIGS. 25A, 25B, and 25C.

In various embodiments and/or usage scenarios, instructions aredistributed to PEs, e.g., under control of software (such as ConnectionServer(s) SW 220, Misc SW on FPGAs 250, and/or Task SW on PEs 260 ofFIG. 2). In various embodiments and/or usage scenarios, a PE operatingas a master PE (e.g., any PE of PEs 122) distributes instructions and/orany portions of configuration information to one or more slave PEs(e.g., any PE of PEs 122, including the master PE) via the fabric. Insome embodiments, the distribution is via wavelets on one or morepredetermined colors (e.g. color zero) and/or in accordance with apredetermined fixed routing pattern. In some other embodiments, thedistribution is via wavelets on one or more selected colors (e.g.,selected by a program). In various embodiments, the wavelets arereceived by one or more PEs operating as slave PEs and written torespective instances of Memory 854 for subsequent fetch and execution.

Dec 840 is enabled to determine one or more characteristics ofinstructions, according to various embodiments and/or usage scenarios.For example, Dec 840 is enabled to parse instructions into an opcode(e.g., Opcode 2512 of FIG. 25A) and zero or more operands (e.g., sourceand/or destination operands). For another example, Dec 840 is enabled toidentify an instruction according to instruction type (e.g., a branchinstruction, or a multiply-accumulate instruction, and so forth). Foryet another example, Dec 840 is enabled to determine that an instructionis a specific instruction and activates one or more signals accordingly.

Dec 840 is coupled to Picker 830 via Terminate 812 and is enabled tosignal that one of the decoded instructions is a terminate instructionthat ends a task (e.g., the terminate instruction is the lastinstruction of the instructions executed in response to a task initiatedin response to the selected wavelet).

In some scenarios, Dec 840 is enabled to decode a branch instruction.Examples of branch instructions include: conditional branch instructionsthat conditionally modify PC 834 and jump instructions thatunconditionally modify PC 834. A branch instruction is executed by I-Seq836 and optionally and/or conditionally modifies PC 834. In somescenarios, a branch instruction implements software control flow (e.g.,a loop) by conditionally modifying PC 834.

In response to decoding an instruction (e.g., a multiply-accumulateinstruction), Dec 840 is enabled to transmit an opcode to Data Path 852.Dec 840 is coupled to DSRs 846 and enabled to transmit one or moreoperand identifiers to DSRs 846. Dec 840 is also coupled to D-Seq 844and enabled to transmit one or more operand type identifiers to D-Seq844.

DSRs 846 comprise registers that hold Data Structure Descriptors (DSDs)and is coupled to and enabled to send one or more DSDs to D-Seq 844. Insome embodiments, DSRs comprise source DSRs, destination DSRs, extendedDSRs, and stride registers. In response to receiving an operandidentifier from Dec 840, DSRs 846 is enabled to read the DSD specifiedby the operand identifier, and to transmit the DSD to D-Seq 844. Invarious embodiments, DSRs 846 is enabled to receive up to two sourceoperand identifiers and one destination operand identifier, read twosource DSRs and one destination DSR, and transmit two source DSDs andone destination DSD to D-Seq 844. In some embodiments, the CE is enabledto explicitly write a DSD to DSRs from memory in response to load DSRinstructions and the CE is enabled to explicitly write a DSD to memoryfrom DSRs in response to store DSR instructions. In some embodiments,DSRs 846 is coupled to and enabled to receive data from and transmitdata to Memory 854.

In some embodiments, DSRs 846 comprise three sets of DSRs: 12 DSRs forsource0 operands (sometimes referred to as S0DSRs), 12 DSRs for source1operands (sometimes referred to as S1DSRs), and 12 DSRs for destinationoperands (sometimes referred to as DDSRs). In addition, DSRs 846 alsocomprises six extended DSRs (sometimes referred to as XDSRs) and sixstride registers. In some embodiments, DSRs comprise 48 bits, XDSRscomprise 51 bits, and stride registers comprise 15 bits. In variousembodiments, respective instructions load 48 bits of data from memory(e.g., D-Store 848 or Memory 854) into respective DSRs (e.g., LDS0WDS,LDS1WDS, and LDDWDS instructions respectively load source0, source1, anddestination DSRs). In various embodiments, respective instructions store48 bits of data from respective DSRs to memory (e.g., STS0WDS, STS1WDS,and STDWDS instructions respectively store source0, source1, anddestination DSRs to memory). In some embodiments, instructions (e.g.,LDXDS) load data from memory into XDSRs and other instructions (e.g.,STXDS) store data from XDSRs to memory. Instructions that move databetween memory and XDSRs (e.g., LDXDS and STXDS) access 64 bits ofmemory, and only use the lower 51 bits. In some embodiments,instructions (e.g., LDSR) load data from memory into stride registers,and other instructions (e.g., STSR) store data from stride registers tomemory. In some embodiments, instructions that move data between memoryand stride registers access 16 bits of memory, and only use the lower 15bits.

D-Seq 844 is also coupled to D-Store 848, RF 842, and Picker 830, and isenabled to initiate accessing vector data at various sources in responseto DSDs received from DSRs 846. In some scenarios (e.g., in response toreceiving a DSD describing one of a 1D memory vector, 4D memory vector,and circular memory buffer), D-Seq 844 is enabled to calculate asequence of memory addresses to access (e.g., in Memory 854 and/orD-Store 848). In some other scenarios, (e.g., in response to receiving aDSD describing a fabric input), D-Seq 844 is enabled to initiate readingfabric data from one of Input Qs 897 via Picker 830. In yet otherscenarios, (e.g., in response to receiving a DSD describing a fabricoutput), D-Seq 844 is enabled to initiate transforming data intowavelet(s) and transmitting wavelet(s) to a fabric coupling via OutputQueues 859 and On Ramp 860. In some embodiments, D-Seq 844 is enabled tosimultaneously access vector data at three sources (e.g., read vectordata from memory, read vector data from a fabric input, and write vectordata to a fabric output).

In some embodiments, D-Seq 844 is enabled to access data in one or moreregisters in RF 842 (e.g., an instruction with one or more inputoperands and/or one output operand). In some scenarios, D-Seq 844 isenabled to request operands from registers in RF 842. In yet otherscenarios, D-Seq 844 is enabled to request data from a register (e.g.,an index) in RF 842 as an input for calculating a sequence of memoryaddresses to access in accordance with a DSD.

In various embodiments, all or any portions of state of PE 800 is mappedin an address space, comprising software visible state (e.g., anycombination of D-Store 848, Memory 854, RF 842, DSRs 846, Output Queues859, and Input Qs 897, Block Bits 899) and state that is not softwareaccessible (e.g., UT State 845). In various embodiments, the addressspace and/or portions of the address space are implemented by one ormore of registers and SRAM. In some embodiments, the address spaces ofmultiple PEs implemented on a single ASIC are mapped to a single addressspace. In some embodiments, each respective PE (e.g., of multiple PEsimplemented on a single ASIC or portion thereof) has a respectiveprivate address space. In some embodiments having private addressspaces, one PE is unable to directly access elements in the addressspaces of other PEs.

Data Path 852 is coupled to RF 842 and D-Store 848. In variousembodiments, any one or more of Memory 854, RF 842, Input Qs 897, andD-Store 848 are enabled to provide data to Data Path 852 (e.g., inresponse to a request from D-Seq 844) and to receive data from Data Path852 (e.g., results of operations). Data Path 852 comprises executionresources (e.g., ALUs) enabled to perform operations (e.g., specified byan opcode decoded and/or provided by Dec 840, according to embodiment).In some embodiments, RF 842 comprises sixteen general-purpose registerssometimes referred to as GPR0-GPR15. Each of the GPRs is 16 bits wideand is enabled to store integer or floating-point data.

Data Path 852 is also coupled via Output Queues 859 and On Ramp 860 tothe router and enabled to send data via Output Queues 859 and On Ramp860 to the router. In various embodiments, Output Queues 859 comprises avirtual queue for each fabric color (e.g., to hold information forwavelets created by Data Path 852 and associated with the respectivecolor), e.g., Q 859.0, . . . , and Q 859.N. In various embodiments, afirst portion of Output Queues 859 are statically or dynamically enabledto hold six wavelets, a second portion of Output Queues 859 arestatically or dynamically enabled to hold two wavelets, and a thirdportion of Output Queues 859 are statically or dynamically enabled tohold zero wavelets.

In some embodiments, Data Path 852 is enabled to write one or morewavelets into one of Output Queues 859 based upon the fabric colorassociated with the one or more wavelets and the mapping of fabriccolors to Output Queues 859. Output Queues 859 is enabled to transmitwavelets via On Ramp 860 to the router (e.g., Router 600 of FIG. 6). Insome embodiments and/or usage scenarios, Output Queues 859 bufferswavelets that are not deliverable to the router (e.g., due tobackpressure or contention). In some embodiments and/or usage scenarios,when one of Output Queues 859 is full, processing that writes fabricpackets to the one of Output Queues 859 is stalled (e.g., by Picker830). In some embodiments and/or usage models, Output Queues 859 iscoupled to a router via On Ramp 837 and enabled to receive backpressureinformation from the router. In various embodiments, the backpressureinformation comprises stall/ready signals for each color, and inresponse to the backpressure information, wavelets corresponding tostalled colors are not sent to the router.

UT State 845 is coupled to Picker 830, Dec 840, D-Seq 844, DSRs 846,Scheduling Info 896, and Output Queues 859 (the foregoing couplings areomitted from the figure for clarity). In various embodiments and orusage scenarios, UT State 845 is used to store and provide informationabout one or more microthreaded instructions. An example of amicrothreaded instruction is an instruction enabling microthreading,e.g., via at least one fabric vector operand with a corresponding UEfield indicating microthreading is enabled. In some embodiments, UTState 845 comprises a data structure of one or more (e.g., eight)entries (e.g., implemented by storage such as SRAM) and enabled to storeand provide information about respective one or more microthreadedinstructions (such as any combination of: the microthreaded instructionitself, an opcode of the microthreaded instruction, one or more operandsof the microthreaded instruction, and one or more DSDs associated withoperands of the microthreaded instruction). In various embodiments, eachrespective entry of UT State 845 is associated with one or more of arespective one of Input Qs 897 and Output Queues 859 (e.g., entry 0 isassociated with Q 897.0 and Q 859.0). In some embodiments, the mappingfrom entries of UT State 845 to ones of Input Qs 897 and Output Queues859 is static and predetermined. UT State 845 is enabled to communicatemicrothreaded instruction information (such as the microthreadedinstruction itself) with Dec 840 and communicate portions of a DSD withone or more of D-Seq 844 and DSRs 846. In some embodiments, informationabout a microthreaded instruction is stored in the entry of UT State 845determined by a microthread identifier from the associated DSD (e.g.,UTID 2102 or UTID 2122). In various embodiments, information about amicrothreaded instruction with a fabric destination operand is stored inan entry determined by UTID 2122. Information about a microthreadedinstruction without a fabric destination is stored in an entrydetermined by UTID 2102 of the src0 operand and an entry determined byUTID 2102 of the src1 operand when there is no src0 operand from thefabric.

In various embodiments and usage scenarios, UT State 845 is enabled toreceive and/or monitor stall information with any one or more of D-Seq844, DSRs 846, Scheduling Info 896, and Output Queues 859. In someembodiments, UT State 845 is enabled to communicate to Picker 830 thatone or more microthreaded instructions are ready for execution, andPicker 830 is enabled to schedule a microthreaded instruction forexecution. In various embodiments and/or usage scenarios, when amicrothreaded instruction from UT State 845 executes, UT State 845 isenabled to communicate instruction information (e.g., the operationand/or one or more operands) to one or more of: Dec 840, D-Seq 844, andData Path 852.

In some embodiments, D-Store 848 is a type of memory that is smaller andmore efficient (e.g., lower joules per bit of data read) than Memory854. In some embodiments, D-Store 848 is a type of memory of relativelylower capacity (e.g., retaining less information) and relatively loweraccess latency and/or relatively higher throughput than Memory 854. Insome scenarios, more frequently used data is stored in D-Store 848,while less frequently used data is stored in Memory 854. In someembodiments, D-Store 848 comprises a first address range and Memory 854comprises a second, non-overlapping address range. In some embodimentsand/or usage scenarios, Memory 854 is considered a first memory enabledto store instructions and any combination of D-Store 848 and RF 842 isconsidered a second memory enabled to store data.

In some embodiments and/or usage scenarios, there is a one to onecorrespondence between virtual queues (e.g., Input Qs 897 and OutputQueues 859) and physical queues (e.g., storage implemented via SRAM),e.g., there is a physical queue for each virtual queue. In some of theone to one embodiments, respective sizes of one or more of the virtualqueues are dynamically managed to vary over time, such as being zero atone time and being a maximum size in accordance with the physical queuesat another point in time. In various embodiments and/or usage scenarios,there is a many to one correspondence between virtual queues andphysical queues, e.g., a single physical queue implements a plurality ofvirtual queues. In various embodiments, there is variously a physical Qfor each color, one or more physical Qs for a predetermined subset ofcolors, and one or more physical Qs for a dynamically determined subsetof colors. In various embodiments, there is variously one or morephysical Qs of a same size (e.g., each enabled to hold a same number ofwavelets) and one or more physical Qs of differing sizes (e.g., eachenabled to hold a different number of wavelets). In various embodiments,there are one or more physical Qs that are variously mapped to virtualQs, each of the virtual Qs being associated with one or more colors. Forexample, there are more virtual Qs than physical Qs. For anotherexample, a first portion of the virtual queues are statically ordynamically enabled to hold six wavelets, a second portion of thevirtual queues are statically or dynamically enabled to hold twowavelets, and a third portion of the virtual queues are statically ordynamically enabled to hold zero wavelets. In some embodiments, one ormore of the physical Qs are implemented by one or more of: registers andSRAM.

In various embodiments, CE 800 is enabled to process instructions inaccordance with a five-stage pipeline. In some embodiments, in a firststage the CE is enabled to perform instruction sequencing, e.g., one ormore of: receiving a wavelet (e.g., in Input Qs 897), selecting awavelet for execution (e.g., by Picker 830), and accessing (e.g., byI-Seq 836) an instruction corresponding to the wavelet. In a secondstage, the CE is enabled to decode (e.g., by Dec 840) the instruction,read any DSR(s) (e.g., from DSRs 846), and compute addresses of operands(e.g., by D-Seq 844 in accordance with a DSD). In a third stage, the CEis enabled to read data from any one or more memories (e.g., Memory 854,RF 842, D-Store 848, and Input Qs 897). In a fourth stage, the CE isenabled to perform an operation specified by the instruction (e.g., inData Path 852) and write results to a register file (e.g., RF 842). In afifth stage, the CE is enabled to write results to any one or morememories, e.g., Memory 854, DSRs 846, D-Store 848. In variousembodiments, in one of the stages the CE is enabled to optionally and/orconditionally provide results to Output Queues 859, and asynchronouslyprovide wavelets to a router.

In some embodiments and/or usage scenarios, elements of the figurecorrespond to an implementation of Compute Element 520 of FIG. 5. Forexample, Off Ramp 820 and Off Ramp 847 in combination correspond to OffRamp 521, and On Ramp 860 and On Ramp 837 in combination correspond toOn Ramp 522.

The partitioning and coupling illustrated in FIG. 8 are illustrativeonly, as other embodiments are contemplated with different partitioningand/or coupling. For example, in other embodiments, RF 842 and DSRs 846are combined into one module. In yet other embodiments, DSRs 846 andData Path 852 are coupled. In some embodiments and/or usage scenarios,elements of Scheduling Info 896 are organized, managed, and/orimplemented by color, e.g., a respective data structure and/or physicalelement or partition thereof is dedicated to color zero, another tocolor one, and so forth.

Task Initiation

FIG. 9A illustrates selected details of an embodiment of processing awavelet for task initiation as flow 900. Conceptually, the processingcomprises initiating a task by determining an address to begin fetchingand executing instructions of the task. The address is determined basedat least in part on information the wavelet comprises.

In some embodiments, processing a wavelet for task initiation begins(Start 901) by selecting a ready wavelet from among, e.g., one or morequeues for processing (Select Ready Wavelet for Task Initiation 902). Insome embodiments, the wavelet is selected based upon one or more of:block/unblock state associated with each queue, active/inactive stateassociated with each queue, color(s) of previously selected wavelets,and a scheduling algorithm.

After selecting the ready wavelet, the wavelet is checked to determineif the wavelet is a control wavelet or a data wavelet (Control/Data?903). If the wavelet is a control wavelet (aka closeout wavelet), then astarting address of a task associated with the control wavelet iscalculated by adding the lower six bits of the index of the wavelet to abase register (Add Lower Index Bits to Base Register to Form InstructionAddress 910). If the wavelet is not a control wavelet, then the waveletis a data wavelet. The starting address of a task associated with thedata wavelet is calculated by adding the base register to the color ofthe wavelet multiplied by four (Add (Color*4) to Base Register to FormInstruction Address 904). The starting address of the task, either ascalculated for a control wavelet or as calculated for a data wavelet,corresponds to a starting address of instructions for the task.

Once the starting address of the instructions has been calculated, theinstructions are fetched from the starting instruction address (FetchInstructions From Memory at Instruction Address 905). One or more of thefetched instructions are decoded and executed (Execute FetchedInstruction(s) 906). Fetching and executing (as illustrated by actions905 and 906) continue (Not Terminate 908) until a Terminate instructionis executed (Terminate 909), and then processing associated with theinitiated task is complete (End 919). In some embodiments, a terminateinstruction is the last instruction associated with processing awavelet. After the initiated task is complete, flow optionally and/orselectively proceeds to process another wavelet for task initiating,beginning with Start 901.

According to various usage scenarios, the executing (Execute FetchedInstruction(s) 906) comprises executing sequential and/or control-flowinstructions, and the instruction address used for fetching variesaccordingly (Fetch Instructions From Memory at Instruction Address 905).

The ready wavelet selected for task initiation is comprised of aparticular color. In some embodiments and/or usage scenarios, once aready wavelet has been selected for task initiation (Select ReadyWavelet for Task Initiation 902), further wavelets, if any, received ofthe particular color are consumed as operands for execution ofinstructions (Execute Fetched Instruction(s) 906). The consuming of thewavelets comprising the particular color as operands continues untilfetching and executing of a terminate instruction (Terminate 909).

In various embodiments and/or usage scenarios, actions of flow 900 areconceptually related to a CE, e.g., CE 800 of FIG. 8. As an example,Block Bits 899 corresponds to block/unblock state associated with eachqueue. Active Bits 898 corresponds to active/inactive state associatedwith each queue. In some embodiments, the active bit of an input queueis set to an active state when a wavelet is written into the inputqueue. As another example, portions of action 902 are performed byPicker 830. Picker 830 selects the oldest wavelet from one of Input Qs897 that is ready (e.g., the associated one of Block Bits 899 isdeasserted and the associated one of Active Bits 898 is asserted),according to a scheduling policy such as round-robin or pick-from-last.In some embodiments and/or usage models, when Picker 830 operates inaccordance with the pick-from-last scheduling policy, Picker 830continues selecting wavelets from a same one of Input Qs 897 that isready until Picker 830 selects a closeout wavelet. The wavelet selectedby Picker 830 comprises a color and a wavelet payload formatted inaccordance with one of FIG. 13A and FIG. 13B, e.g., assertion of ControlBit 1320 (FIG. 13A) or assertion of Control Bit 1340 (FIG. 13B)indicates a closeout wavelet.

As another example, action 903 is performed by elements of CE 800. Ifthe control bit of the wavelet payload (e.g., Control Bit 1320 of FIG.13A) is asserted (determined e.g., by Picker 830), then the wavelet is acontrol wavelet. Subsequently, action 910 is performed by CE 800, suchas by Picker 830 adding contents of Base 890 to the six lowest bits ofLower Index Bits 1321.1 of FIG. 13A to form the instruction fetchaddress for instructions of the task associated with the controlwavelet. Picker 830 then provides the instruction fetch address to PC834. If the control bit of the wavelet payload (e.g., Control Bit 1320of FIG. 13A) is deasserted (determined e.g., by Picker 830), then thewavelet is a data wavelet. Subsequently, action 904 is performed by CE800, such as by Picker 830 adding contents of Base 890 to the color ofthe wavelet (e.g., corresponding to Color 1324 of FIG. 13A and FIG. 13B)multiplied by 4 to form the instruction fetch address for instructionsof the task associated with the data wavelet. Picker 830 then providesthe instruction fetch address to PC 834.

As another example, action 905 is performed by elements of CE 800, e.g.,PC 834, I-Seq 836, and Memory 854. Action 906 is performed by elementsof CE 800, e.g., Dec 840, D-Seq 844, Memory 854, RF 842, and Data Path852, among others. Execution comprises execution of a terminateinstruction. An example of a terminate instruction is an instructionwith a terminate bit asserted. In the context of the example, when Dec840 decodes a terminate instruction, Dec 840 signals Picker 830 viaTerminate 812 that the wavelet is finished, and Picker 830 selectsanother wavelet for processing, corresponding, e.g., to action 902.

In various embodiments and/or usage scenarios, all or any portions ofelements of Processing a Wavelet for Task Initiation 900 conceptuallycorrespond to all or any portions of executions of instructions of TaskSW on PEs 260 of FIG. 2.

In various embodiments and/or usage scenarios, all or any portions ofthe actions comprising flow 900 conceptually variously correspond to allor any portions of flow 1500 of FIG. 15 and/or flow 1600 of FIG. 16.E.g., action 902 comprises all or any portions of action 1602, andactions 903, 904, 910, 905, and 906 comprise all or any portions ofaction 1603.

FIG. 9B illustrates selected details of an embodiment of task activatingas flow 920. Conceptually, the task activating comprises activating onor more colors, resulting in the colors becoming selectable forexecution, and then choosing a color (e.g. one of the activated colors)and initiating a task corresponding to the color.

In some embodiments, flow for task activating begins (Start 921) byperforming an activate operation for one or more colors (ActivateOperation for Color(s) 923). The activate operation is responsive to,e.g., an instruction or one of a set of events. In response to theactivate operation, corresponding colors are activated, making themselectable for execution (Activate Color(s) 924). Then a color that isselectable for execution is chosen by the picker (Picker Selects Color925). The task corresponding to the chosen color is initiated and thechosen color is deactivated (Initiate Task, Deactivate Color 926). Taskinitiation comprises determining a starting address for the task andfetching and executing instruction beginning at the starting address.Flow is then complete (End 929).

The instruction the activate operation is responsive to comprises anactivate instruction. The activate instruction specifies the one or morecolors to activate. The colors to activate are variously specified byone or more of an immediate value (e.g. a 6-bit field specifying asingle color to activate) in the activate instruction, a registerspecified by the activate instruction, or other information. In someembodiments and/or usage scenarios, if an activate instruction source isnot an immediate, then new task selection is stalled until the activateinstruction completes.

In some embodiments and/or usage scenarios, the set of events theactivate operation is responsive to comprises completing processing fora fabric vector that enables microthreading. For example, a fabricvector is processed in accordance with a fabric input Data StructureDescriptor (DSD). The fabric input DSD specifies that microthreading isenabled and the fabric input DSD further specifies a color to activateresponsive to completing processing of the fabric vector. The color isactivated in response to the completing processing of the fabric vector.For another example, a fabric vector is processed in accordance with afabric output DSD. The fabric output DSD specifies that microthreadingis enabled and the fabric output DSD further specifies a color toactivate responsive to completing processing of the fabric vector. Thecolor is activated in response to the completing processing of thefabric vector.

In some embodiments and/or usage scenarios, the set of events theactivate operation is responsive to further comprises pushing and/orpopping an element from a circular buffer in accordance with a circularmemory buffer DSD having an associated circular memory buffer eXtendedDSD (XDSD). The circular memory buffer XDSD has respective fields tospecify colors to activate responsive to pushing an element onto thecircular buffer and popping an element off of the circular buffer. Therespective color is activated in response to the pushing and/or thepopping.

In some embodiments and/or usage scenarios, activating a color comprisessetting an indicator corresponding to the color to an activated stated,and making a color inactive comprises setting the indicator to aninactivated state. In some embodiments and/or usage scenarios, theindicator comprises a bit, assertion of the bit indicates the activatedstate, and deassertion of the bit indicates the inactivated state, andthere is a corresponding bit for each color.

In various embodiments and/or usage scenarios, actions illustrated inFIG. 9B are applicable to fabric colors and/or local colors.

In various embodiments and/or usage scenarios, actions of flow 920 areconceptually related to a CE, e.g., CE 800 of FIG. 8. For example,activating/deactivating a color is performed by asserting/deasserting acorresponding one of Active Bits 898. For another example, PickerSelects Color 925 is performed by Picker 830. In various embodimentsand/or usage scenarios, all or any portions of the actions comprisingflow 920 conceptually variously correspond to all or any portions offlow 900 of FIG. 9A, e.g., action 926 comprises all or any portions ofactions 904, 905, and 906 of FIG. 9A.

Fabric Input Data Structure Descriptor 2100 (FIG. 21A) is an examplefabric input DSD having a field (UE 2103) to specify enablingmicrothreading and a field (AC 2105) to specify a color to activateresponsive to completing processing of the fabric vector described bythe fabric input DSD. Fabric Output Data Structure Descriptor 2120 (FIG.21B) is an example fabric output DSD having a field (UE 2123) to specifyenabling microthreading and a field (AC 2125) to specify a color toactivate responsive to completing processing of the fabric vectordescribed by the fabric output DSD. Circular Memory Buffer DataStructure Descriptor 2180 (FIG. 21E) is an example circular memorybuffer DSD having an associated circular memory buffer eXtended DSD(XDSD) having respective fields to specify colors to activate responsiveto pushing an element onto the circular buffer and popping an elementoff of the circular buffer. Circular Memory Buffer Extended DataStructure Descriptor 2210 (FIG. 22A) is an example circular memorybuffer eXtended DSD (XDSD) having respective fields (Push Color 2215 andPop Color 2216) to specify colors to activate responsive to pushing anelement onto the circular buffer and popping an element off of thecircular buffer.

Task Block and Unblock

In various embodiments and/or usage scenarios, the instruction set of CE800 comprises block and unblock instructions, and instructions enabledto perform an activate operation (e.g., an activate instruction), usefulfor, inter alia, task synchronization. Task SW on PEs 260 of FIG. 2 isenabled to use the block and unblock instructions, and instructionsenabled to perform an activate operation to selectively locally shapevarious aspects of fabric operation in pursuit of various goals. E.g.,Task SW on PEs 260 is enabled to use these instructions to perform oneor more of orchestrating computations and/or communications of one ormore tasks, dataflow control, manage dependencies and/or prioritieswithin and between tasks, throttle (stall/resume) task activities toindirectly manage the queues to have generally equal average rates ofproduction and consumption, and implement software interlocks tosynchronize intermediate data converging from multiple sources and/orpaths of diverse latencies (e.g., as might arise in forward and/orbackward pass computations near the boundary of a neural network layer,aspects of which are variously illustrated in FIG. 11, FIG. 12 and FIGS.28A-28E).

FIG. 9C illustrates selected details of an embodiment of blockinstruction and unblock instruction execution as flow 940. Conceptually,executing a block instruction specifying a particular color results inone or more of the following, according to embodiment and/or usagescenario. Instructions associated with the particular color areprevented from executing at least until execution of an unblockinstruction specifying the particular color. Wavelets comprising theparticular color are not selected at least until execution of an unblockinstruction specifying the particular color. An activated color matchingthe particular color is not selected (and hence initiating acorresponding task is not performed) at least until execution of anunblock instruction specifying the particular color. Microthreadsassociated with the particular color are prevented from executing atleast until execution of an unblock instruction specifying theparticular color.

Referring to the figure, executing an instruction begins (Start 941) byfetching the instruction from memory and decoding the instruction(Fetch, Decode Instruction 942). If the instruction decodes to a blockinstruction (Block Instruction? 943), then a block operation isperformed (Block Color(s) 944). The source operand of the blockinstruction specifies one or more colors to block with respect toinstruction processing associated with blocked/unblocked colors. Invarious embodiments and/or usage scenarios, the block operation isperformed by setting one or more block indicators to a blocked state forthe one or more colors specified by the source operand, and execution iscomplete (End 949). In various scenarios, the source operand variouslyspecifies blocking a single color, blocking all colors, and blocking anarbitrary plurality of colors. In subsequent operation, waveletscomprised of colors that are blocked are not selected for processing.

If the instruction decodes to an unblock instruction (UnblockInstruction? 945), then an unblock operation is performed (UnblockColor(s) 946). The source operand of the unblock instruction specifiesone or more colors to unblock with respect to instruction processingassociated with blocked/unblocked colors. In various embodiments and/orusage scenarios, the unblock operation is performed by setting a blockindicator to an unblocked state for the one or more colors specified bythe source operand, and execution is complete (End 949). In variousscenarios, the source operand variously specifies unblocking a singlecolor, unblocking all colors, and unblocking an arbitrary plurality ofcolors. In subsequent operation, wavelets comprised of colors that areunblocked are selectable for processing.

If the instruction decodes to an instruction that is not a blockinstruction and that is not an unblock instruction, then the instructionis otherwise executed (Execute Instruction 947) and execution iscomplete (End 949).

In some embodiments, if the source operand of a block instruction is animmediate (e.g., an 8-bit immediate), then the value of the immediatespecifies the color to be blocked. In various embodiments, a blockinstruction with particular operands blocks multiple colors. If thesource operand is not an immediate, then all colors are blocked untilthe block instruction completes.

In some embodiments, the source operand of an unblock instruction is animmediate (e.g., an 8-bit immediate) and the value of the immediatespecifies the color to be unblocked. In various embodiments, an unblockinstruction with particular operands unblocks multiple colors.

In various embodiments and/or usage scenarios, all or any portions ofany one or more of elements of Block and Unblock Instruction ProcessingFlow 940 correspond conceptually to and/or are related conceptually tooperations performed by and/or elements of a compute element, such asall or any portions of a CE of a PE, e.g., Compute Element 520 of FIG. 5and/or CE 800 of FIG. 8.

As an example, Block Bits 899 comprise a bit for each color (e.g., asentries in a table, or as a bit-mask). The block operation (BlockColor(s) 944) is performed by setting Block Bits 899 to a specificblocked state (e.g., ‘1’) for the one or more colors specified by thesource operand. In some embodiments, Picker 830 selects a wavelet forprocessing from a color where Block Bits 899 match an unblocked state(e.g., ‘0’). As another example, the unblock operation (Unblock Color(s)946) is performed by setting Block Bits 899 to a specific unblockedstate (e.g., ‘0’) for the one or more colors specified by the sourceoperand. In some embodiments, Picker 830 selects a wavelet comprising acolor where Block Bits 899 match an unblocked state (e.g., ‘0’).

In some embodiments, portions of Block and Unblock InstructionProcessing Flow 940 correspond to portions of Processing a Wavelet forTask Initiation 900 of FIG. 9A. As an example, actions 942 943, 944,945, 946, and 947 correspond to portions of actions 905 and 906 of FIG.9A.

In various embodiments and/or usage scenarios, all or any portions ofelements of Block and Unblock Instruction Processing Flow 940conceptually correspond to all or any portions of executions ofinstructions of Task SW on PEs 260 of FIG. 2.

High-Level Dataflow

FIGS. 10A and 10B illustrate selected details of high-level dataflowoccurring in an embodiment mapping multiple instances of a single neuronto respective sets of processing elements, e.g., as determined by Neuronto PE Mapping SW 212 of FIG. 2 executing on Placement Server(s) 150 ofFIG. 1. FIG. 10A abstractly illustrates an internal neural networkportion 1040 of a larger neural network, such as that of FIG. 17. Neuralnetwork portion 1040 has three neurons in a first neuron layer (on theleft) and three neurons in a second neuron layer (on the right). Thefirst neuron layer includes Neuron A 1041, Neuron B 1042, and Neuron C1043. The second neuron layer includes Neuron D 1044, Neuron E 1045, andNeuron F 1046. Each of activation aA 1061 from Neuron A 1041, activationaB 1062 from Neuron B 1042, and activation aC 1063 from Neuron C 1043,when respectively non-zero, are broadcast into the second neuron layerand communicated to Neuron D 1044, Neuron E 1045, and Neuron F 1046 inaccordance with the topology as illustrated. Each of activation aD 1064from Neuron D 1044, activation aE 1065 from Neuron E 1045, andactivation aF 1066 from Neuron 1046, when respectively non-zero, arebroadcast into the next layer (not illustrated). Only non-zeroactivations are broadcast so no wasted compute is used for zeroactivations. In this way, activation sparsity is accumulated over thewafer to improve efficiency and reduce power consumption.

FIG. 10B illustrates processing element array portion 1060 of a largerprocessing element array, such as that of wafer 412 of FIG. 4. Likenumbered elements of FIG. 10B correspond to like numbered elements ofFIG. 10A. Neuron D 1044 is mapped to PE0 1070, PE3 1073, and PE6 1076via respective locally stored distributions of weights wAD 1080, wBD1083, and wCD 1086. Neuron E 1045 is mapped to PE1 1071, PE4 1074, andPE7 1077 via respective locally stored distributions of weights wAE1081, wBE 1084, and wCE 1087. Neuron F 1046 is mapped to PE2 1072, PE51075, and PE8 1078 via respective locally stored distributions ofweights wAF 1082, wBF 1085, and wCF 1088.

Non-zero activation aA 1061 from Neuron A 1041 triggers lookups ofstored weights wAD 1080, wAE 1081, and wAF 1082. PE0 1070, PE1 1071, andPE2 1072 perform respective local multiply and accumulates of therespective local neuron weights with the incoming activation aA 1061from Neuron A 1041 to produce respective local partial sums. Non-zeroactivation aB 1062 from Neuron B 1042 triggers lookups of stored weightswBD 1083, wBE 1084, and wBF 1085. PE3 1073, PE4 1074, and PE5 1075perform respective local multiply and accumulates of the respectivelocal neuron weights with the incoming activation aB 1062 from Neuron B1042 to produce respective local partial sums. Non-zero activation aC1063 from Neuron C 1043 triggers lookups of stored weights wCD 1086, wCE1087, and wCF 1088. PE6 1076, PE7 1077, and PE8 1078 perform respectivelocal multiply and accumulates of the respective local neuron weightswith the incoming activation aC 1063 from Neuron C 1043 to producerespective local partial sums. The local partial sums of PE0 1070, PE31073, and PE6 1076 are accumulated to produce a final sum, an activationfunction is performed, and if non-zero, activation aD 1064 is broadcastto the next layer. The local partial sums of PE1 1071, PE4 1074, and PE71077 are accumulated to produce a final sum, an activation function isperformed, and if non-zero, activation aE 1065 is broadcast to the nextlayer. The local partial sums of PE2 1072, PE5 1075, and PE8 1078 areaccumulated to produce a final sum, an activation function is performed,and if non-zero, activation aF 1066 is broadcast to the next layer.

In FIG. 10B, activations aA 1061, aB 1062, aC 1063, aD 1064, aE 1065, aF1066, are represented as being communicated via respective bus segmentsand the partial sum accumulations and activation functions correspondingto Neuron D 1044, Neuron E 1045, and Neuron F 1046, are represented asbeing respectively performed by PSA 1090, PSA 1091, and PSA 1092. Insome embodiments and/or usage scenarios, the bus segments and PSA 1090,PSA 1091, and PSA 1092 of FIG. 10B are abstractions and the partial sumaccumulations and activation functions are performed by variousprocessing elements, e.g., as also determined by Neuron to PE Mapping SW212 executing on Placement Server(s) 150, and the partial sums andactivations are communicated as wavelets (see, e.g., FIGS. 13A-16 andsection “Wavelets”) via virtual channels over the couplings between theprocessing elements.

Example Workload Mapping and Exemplary Tasks

Conceptually, Deep Learning Accelerator 400 (FIG. 4) is a programmablecompute fabric (see, e.g., FIGS. 5-8 and section “Processing Element:Compute Element and Router”). For example, the compute element of eachPE 499 element is enabled to execute sequences of instructions of tasks(such as conceptually corresponding to all or any portions of executionsof instructions of Task SW on PEs 260 of FIG. 2), and the respectiverouter element of each PE 499 is configurable to route wavelets betweenthe PEs. The programmable compute fabric enables mapping of workloadsonto the compute fabric in various manners. Described following is anexample high-level mapping of a workload to the compute fabric toillustrate various techniques and mechanisms implemented by the computefabric.

The workload is deep neural network training, implemented via SGD. Thedeep neural network comprises a plurality of layers of neurons. Theworkload has three mega-phases: a forward pass, a delta pass, and achain pass. The forward pass propagates activations in a forwarddirection. The delta pass propagates deltas in a backward direction. Thechain pass calculates gradients based on the deltas as the deltas aregenerated in the delta pass. The three mega-phases have approximately asame amount of compute.

FIG. 4 illustrates an example mapping of the mega-phases to the PEs.Each layer is implemented by blocks of PEs allocated from the computefabric (aka ‘placed’) back-to-back (e.g., in a horizontal dimension).Data movement propagates to the end of the fabric during the forwardpass (Forward 401), and then circles back in the reverse directionduring the delta pass (Delta 402) and chain pass (Chain 403). Theplacement is directed to reduce data movement since the forward passsaves activations to be used by the delta pass and the chain pass. Inthe example, all the PEs are time shared three ways between the threemega-phases, with each mega-phase using approximately a same amount ofcompute. In some circumstances, an entire chain of PEs performing thepasses operates as a pipeline such that each layer is a pipe stage(taking roughly a same amount of time to complete) and each activationof a mini-batch fills the pipeline.

In some embodiments and/or usage scenarios, within a set of the PEsmapped to a single one of the layers, the weights of the single layerare distributed across the PEs such that a single neuron is mapped tomultiple PEs. Splitting a single neuron across multiple PEs, in somecircumstances, provides a load balancing benefit and provides acommunication partitioning benefit (see, e.g., FIGS. 10A-10B and section“High-Level Dataflow” as well as FIGS. 17-20 and section “NeuronSmearing”).

Conceptually, processing proceeds as follows (see Forward 401 of FIG.4). Activations are broadcasted into the layer along the horizontalaxis. Activations are received by the PEs and trigger a lookup of theassociated weights that are stored local to the PEs (corresponding tothe neurons mapped to the PEs). Only non-zero activations arebroadcasted, so no compute is wasted for zero activations (an example ofactivation sparsity harvesting). Each PE performs a local multiply andaccumulate of the incoming activation with all the neuron weightsproducing local partial sums. Since the weights of each neuron aredistributed to multiple PEs, partial sums are then accumulated acrossthe PEs in the vertical direction, in accordance with the neuron weightdistribution. After the partial sums are accumulated producing a finalsum, the activation function is performed and all new non-zeroactivations are broadcast to the next layer.

The delta pass (see Delta 402 of FIG. 4) and the chain pass (see Chain403 of FIG. 4) follow a data flow similar to that of the forward pass.In some embodiments and/or usage scenarios, the delta pass and the chainpass are placed offset by one layer so the activations are stored in thesame layers as the weights used in the backward direction. Activationsare stored by the receiving layer such that in the delta pass and thechain pass, the activations are used directly without additionalcommunication. In addition to storing activations, a weight transpose isperformed to implement the delta pass. The weight transpose, in someembodiments and/or usage scenarios, is implemented by replicating theweights, using additional memory capacity and additional communicationwhen updating the weights. In some embodiments and/or usage scenarios,the weight transpose is implemented by transposing the delta broadcastin the vertical dimension.

FIG. 11 illustrates an embodiment of tasks (see, e.g., FIGS. 9A-9C andsections “Task Initiation” and “Task Block and Unblock”) as used in aforward pass state machine, including dependency management viacloseouts. In some embodiments and/or usage scenarios, each of the PEsimplements an instantiation of the state machine. In some embodimentsand/or usage scenarios, various portions of the state machine areimplemented by respective PEs (see, e.g., FIGS. 17-20 and section“Neuron Smearing”). There are four tasks in the state machine:f_rxact:acc 1101, f_rxact:close 1102, f_psum:prop 1103, and f_txact:tx1104. Conceptually, activations arrive from a PE to the “left” of theinstant PE (corresponding to a previous layer). Incoming (non-closeout)activations from, e.g., a prior layer on the activation broadcast wire(Activations from Prior Layer 1111) trigger f_rxact:acc 1101. Theinstant PE executes instructions of the task, looking up (e.g., frommemory local to the instant PE) the weights associated with theactivation and performing the local weight multiply and accumulate intopartial sums. Control flow dependencies exist between f_rxact:acc 1101and fpsum:prop 1103 (Flow 1113). Example data structures the taskreferences are wrow, fpsum, and fact.

An incoming activation closeout on the activation broadcast wire(Closeouts from Prior Layer 1112) triggers f_rxact:close 1102. Thecloseout signals the end of all activations for the current wavefront.The instant PE executes instructions of the task, starting the partialsum accumulation ring with the partial sums in a start list of theinstant PE (Start Psums 1116). Example data structures the taskreferences are fpsum_acc_mem, and fpsum_acc_fab.

An incoming partial sum (Prop Psums 1130) triggers fpsum:prop 1103. Theinstant PE executes instructions of the task, adding the incomingpartial sum to the local partial sum of the instant PE, and thenforwarding the result to the next hop on the ring (Prop Psums 1131). Ifthe instant PE is the end of the ring, then the final sum is generated.In some embodiments and/or usage scenarios, additional processing isperformed to prevent deadlock. Example data structures the taskreferences are fpsum_acc_mem, fpsum_acc_fab, and f_txact wake.

When there are queued activations to transmit, f_txact:tx 1104 isself-triggered (Wake 1114), e.g., via the instant PE sending a waveletto itself. The instant PE executes instructions of the task, de-queuingan activation and transmitting the activation on the broadcast wire tothe next layer (Activations to Next Layer 1121). When more items remainin the queue, the instant PE reschedules the task (Reschedule 1115),e.g., via the instant PE sending a wavelet to itself. When the queue isempty, the instant PE sends a closeout wavelet to close the wavefront(Closeouts to Next Layer 1122).

The activations (incoming and outgoing) and the partial sums (incomingand outgoing), as well as the closeout wavelets are communicated aswavelets (see, e.g., FIGS. 13A-16 and section “Wavelets”). In someembodiments and/or usage scenarios, one or more of the waveletscorrespond to one or more elements of fabric vectors as described by oneor more DSDs and/or XDSDs.

Data structures for the various state machines are referenced via aplurality of DSDs stored in respective DSRs (see, e.g., FIGS. 21A-24 andsection “Vectors and Data Structure Descriptors”), as described by thefollowing table.

Data Structure DSR Name Description DS1 Wrow Weight matrix, rows DS2Wcol Weight matrix, cols (points to same data as DS2) DS3 Fpsum Forwardpartial sum vector-full vector of all psums Length: number of neuronsStride: 1 DS4 fpsum_acc_mem Forward partial sum vector-subset for psumaccumulate Same data as psum but organized as 2d array Length: number ofneurons in subset Stride: 1 D55 fpsum_acc_fab Forward partial sumvector-subset for psum accumulate Fabric type: col:ep = f_psum:propLength: number of neurons in subset D56 Fact Forward activation storagevector Length: 1 Stride: 1 D57 fact_fab Forward activation fabrictransmit Fabric type: col:ep = f_txact:acc Length: 1 D58 f_txact_wakeSelf reschedule wake up wavelet Fabric type: col:ep = f_txact:tx D59fact_close_fab Forward activation close out fabric transmit Fabric type:col:ep = f_txact:close Length: 1

The foregoing example workload mapping is with respect to SGD. However,the techniques are readily applicable to MBGD and CPGD, with and withoutRCP.

In some embodiments and/or usage scenarios, all or any portions of theactions of FIG. 11 correspond or are related conceptually to operationsperformed by and/or elements of PEs 122 of FIG. 1. In some embodimentsand/or usage scenarios, all or any portions of elements of FIG. 11conceptually correspond to all or any portions of executions ofinstructions of Task SW on PEs 260 of FIG. 2.

FIG. 12 illustrates selected details of an embodiment of flow associatedwith activation accumulation and closeout, followed by partial sumcomputation and closeout as Activation Accumulation/Closeout and PartialSum Computation/Closeout 1200.

Flow begins (Start 1201). Activations are received (Receive Activation1202) and accumulated (Accumulate Activations 1203), e.g., as processedby f_rxact:acc 1101 of FIG. 11. In response to receiving an activationcloseout (Receive Activation Closeout 1204), partial sum computation ona ‘ring’ of PEs is initiated (Start Partial Sum Ring 1205), e.g., asperformed by f_rxact:close 1102 of FIG. 11 and indicated by Start Psums1116 of FIG. 11. An example ring of PEs is illustrated in FIG. 10B asPE0 1070, PE3 1073, and PE6 1076, with corresponding partial sumaccumulation illustrated by PSA 1090. In some embodiments and/or usagescenarios, Receive Activation Closeout 1204 concludes accumulatingactivations and enforces ordering with respect to initiating partial sumcomputation, e.g., ensuring that all activations are received andaccumulated prior to initializing partial sum computation. An (input)partial sum is received by an instant PE (Receive Partial Sum 1206),added to a partial sum computed by the instant PE (Compute Partial Sum1207) and a result of the addition forms an (output) partial sum that istransmitted to a next PE of the ring (Transmit Partial Sum 1208). Thereception, adding, and transmission are performed, e.g., by f_psum:prop1103 of FIG. 11 and the input/output partial sums are as indicatedrespectively by Prop Psums 1130 and Prop Psums 1131 also of FIG. 11.When a final sum has been computed by completion of the partial sumcomputations on the ring of PEs, activations for output to the nextlayer are produced and transmitted (Transmit Activations 1209), e.g., byf_txact:tx 1104 of FIG. 11 and as indicated by Activations to Next Layer1121 also of FIG. 11. When all activations have been transmitted, acloseout is transmitted (Transmit Closeout 1210), e.g., also byf_txact:tx 1104 of FIG. 11 and as indicated by Closeouts to Next Layer1122 also of FIG. 11. Flow is then complete (End 1211). In someembodiments and/or usage scenarios, Transmit Closeout 1210 concludestransmitting closeouts and enforces ordering transmitting activationswith respect to further processing, e.g., ensuring that all activationsare transmitted before further processing.

In some embodiments and/or usage scenarios, closeouts conclude otherportions of a neural network, e.g., transmitting deltas.

In some embodiments and/or usage scenarios, all or any portions of theactions of Activation Accumulation/Closeout and Partial SumComputation/Closeout 1200 correspond or are related conceptually tooperations performed by and/or elements of PEs 122 of FIG. 1. In someembodiments and/or usage scenarios, all or any portions of elements ofActivation Accumulation/Closeout and Partial Sum Computation/Closeout1200 conceptually correspond to all or any portions of executions ofinstructions of Task SW on PEs 260. In various embodiments and/or usagescenarios, a closeout (e.g., associated with action 1210) is an exampleof a control wavelet.

Wavelets

FIG. 13A illustrates selected details of an embodiment of a sparsewavelet, as Sparse Wavelet 1301. Sparse Wavelet 1301 comprises SparseWavelet Payload 1302 and Color 1324. Sparse Wavelet Payload 1302comprises Index 1321, Sparse Data 1322, and Control Bit 1320. Index 1321comprises Lower Index Bits 1321.1 and Upper Index Bits 1321.2.

In some embodiments, Sparse Data 1322 comprises a field for a 16-bitfloating-point number or a 16-bit integer number. In various scenarios,Sparse Data 1322 variously represents a weight of a neural network, aninput or stimulus of a neural network, an activation of a neuralnetwork, or a partial sum of a neural network.

In some embodiments, Index 1321 comprises a 16-bit field. In somescenarios, Index 1321 is an integer number and is an index thatexplicitly indicates a specific neuron of a neural network. In someembodiments, Lower Index Bits 1321.1 is six bits, and Upper Index Bits1321.2 is 10 bits.

In some embodiments, Control Bit 1320 is 1-bit field. In some scenarios,Control Bit 1320 indicates whether Sparse Wavelet Payload 1302 triggerscontrol activity or data activity. In some scenarios, control activitycomprises computing the last activation of a neuron and data activitycomprises computing activations of a neuron that are not the lastactivation. In some embodiments and/or usage scenarios, the controlactivity comprises a closeout activity, such as associated with any oneor more of Closeouts from Prior Layer 1112 and/or Closeouts to NextLayer 1122 of FIG. 11, as well as any one or more of Receive ActivationCloseout 1204 and/or Transmit Closeout 1210 of FIG. 12.

In some embodiments, Color 1324 comprises a 5-bit field. In someembodiments, a color corresponds to a virtual channel over a sharedphysical channel, such as via routing in accordance with the color. Insome scenarios, a color is used for a specific purpose such as sendingconfiguration information to processing elements or sending input of aneural network to a neuron that is mapped to a processing element.

FIG. 13B illustrates selected details of an embodiment of a densewavelet, as Dense Wavelet 1331. Dense Wavelet 1331 comprises DenseWavelet Payload 1332 and Color 1344. Dense Wavelet Payload 1332comprises Dense Data 1343.1, Dense Data 1343.2, and Control Bit 1340.

In some embodiments, Control Bit 1340 is a 1-bit field and isfunctionally identical to Control Bit 1320.

In some embodiments, Color 1344 comprises a 5-bit field and isfunctionally identical to Color 1324.

In some scenarios, Dense Data 1343.1 and Dense Data 1343.2 comprisefields for respective 16-bit floating-point numbers or respective 16-bitinteger numbers. In various scenarios, Dense Data 1343.1 and Dense Data1343.2 variously represent weights of a neural network, inputs orstimuli of a neural network, activations of a neural network, or partialsums of a neural network. In some scenarios, Dense Data 1343.1 and DenseData 1343.2 collectively comprise a 32-bit floating-point number (e.g.,Dense Data 1343.1 comprises a first portion of the 32-bit floating-pointnumber and Dense Data 1343.2 comprises a second portion of the 32-bitfloating-point number).

In various embodiments and/or usage scenarios, usage of sparse waveletsvs. dense wavelets is variously predetermined, dynamically determined,and/or both. In various embodiments and/or usage scenarios, usage ofsparse wavelets vs. dense wavelets is determined by software.

FIG. 14 illustrates selected details of an embodiment of creating andtransmitting a wavelet, as Wavelet Creation Flow 1400. Actions ofWavelet Creation Flow 1400 are performed by various agents. Atransmitting PE comprises a CE that performs actions 1403-1409, asillustrated by CE of Transmitting PE 1420. The transmitting PE furthercomprises a router that performs action 1411, as illustrated by Routerof Transmitting PE 1430. A receiving PE comprises a router that performsaction 1412, as illustrated by Router of Receiving PE 1440.

Creating and transmitting a wavelet begins (Start 1401) by initializingat least one transmitting PE and one or more receiving PEs, as well asany PEs comprising routers implementing a fabric coupling thetransmitting PEs and the receiving PEs (Initialize PEs 1402). Each ofthe PEs comprises a respective router (e.g., Router 510 of FIG. 5) and arespective CE (e.g., Compute Element 520 of FIG. 5). In some scenarios,initializing a PE enables the CE of the PE to perform computations andenables the router of the PE to transmit, receive, and/or route waveletsover the fabric.

In various embodiments, a DSR holds a DSD comprising information aboutan operand such as location of data elements (e.g., memory, fabricinput, and/or fabric output), number of the data elements (e.g.,length), an address or addresses of the data elements (e.g., startaddress and stride in memory). For fabric output operands (e.g.,wavelets sent via the fabric), the DSR comprises a color for thewavelet(s) on the fabric, a control bit, and optionally a value orlocation of an index.

In some embodiments, the CE of the transmitting PE configures a source(Set Source 1403). In some scenarios, the source is a source DSDdescribing a source operand. In various embodiments, the source DSDdescribes one or more data elements stored in one of: cache and memory.In other embodiments, the source DSD describes one or more data elementsreceived via the fabric (e.g., the data elements are payloads ofwavelets arriving via the fabric). In some other scenarios, the sourcecomprises a source register (e.g., one of RF 842). In yet otherscenarios, the source comprises an immediate specified in aninstruction.

The CE also configures a destination DSD in a destination DSR describingthe location of a destination operand. In various embodiments, thelocation of the destination operand is the fabric (Set Destination(Fabric) DSR 1404). In some embodiments, the destination DSD describesone or more data elements transmitted via the fabric. In variousembodiments, the source and the destination DSDs are configured via oneor more instructions.

Subsequently, the CE fetches and decodes an instruction (e.g., FMACH,MOV, LT16) comprising one or more source operands, an operation, and adestination operand specified by the DSD in the destination DSR(Fetch/Decode Instruction with Destination DSR 1405). In someembodiments, the operand type fields of the instruction specify whetheran operand is specified by a DSD.

The CE reads the destination DSD from the destination DSR and any sourceDSDs in source DSRs (Read DSR(s) 1406). Based on the DSDs, the CEdetermines the type of data structure, the source of the dataelement(s), whether multiple data elements are read together (e.g., fora SIMD operation), and a total number of data elements for each operand.In some scenarios, DSRs are read for one or more of: a source0 operand,a source1 operand, and a destination operand. In some embodiments and/orusage scenarios, the DSRs are read entirely or partially in parallel,and in other embodiments and/or usage scenarios, the DSRs are readentirely or partially sequentially.

The CE of the transmitting PE reads (e.g., from register or memory) thefirst data element(s) specified by the source (Read (Next) DataElements(s) from Queue/Memory 1407) and performs the operation specifiedby the instruction (e.g., multiplication) on the first data element(s).In response to the destination operand being specified as a fabric typeby the destination DSD, the CE creates one or more wavelets. One or moreresults of the operation (e.g., in a form of data elements) are used toform a wavelet payload, based on the destination DSD. The control bit ofthe wavelet payload and the color of the wavelet are specified by thedestination DSD. The wavelet payload and the color are provided to therouter of the transmitting CE (Provide Data Element(s) as Wavelet toOutput Queue 1408). In some embodiments and/or usage scenarios, a singledata element is used to create the payload of a sparse wavelet. In otherembodiments and/or usage scenarios, two data elements are used to createthe payload of a dense wavelet. In various embodiments, four dataelements are used to create the payload of two wavelets. In someembodiments, the number of data elements used is specified by thedestination DSD.

The CE of the transmitting PE determines if additional data element(s)are specified by the destination DSD (More Data Elements? 1409). Ifadditional data element(s) are specified by the destination DSD, thenthe CE creates additional wavelet(s) via actions Read (Next) Source DataElement(s) from Queue/Memory 1407, Provide Data Element(s) as Wavelet toOutput Queue 1408, and More Data Elements? 1409 until no additional dataelement(s) are specified by the destination DSD. If no additional dataelement(s) are specified by the destination DSD, then flow concludes(End 1410). In some embodiments, the wavelets created via action 1408are of the same color as specified by the destination DSR.

The router of the transmitting PE transmits the wavelet(s) in accordancewith the color of the wavelet(s) (Transmit Wavelet(s) to Fabric 1411),in accordance with respective colors of the wavelets. In someembodiments and/or usage scenarios, the transmitting is directly to therouter of the receiving PE. In some embodiments and/or usage scenarios,the transmitting is indirectly to the router of the receiving PE, e.g.,via one or more intervening PEs acting to forward the wavelet(s) inaccordance with the colors. The router of the receiving PE receives thewavelet(s) in accordance with the color (Receive Wavelet(s) from Fabric1412).

In various embodiments, action 1411 is performed asynchronously withrespect to any one or more of actions 1407, 1408, and 1409. For example,a plurality of wavelets is produced by action 1408 before any of theproduced wavelets are transmitted as illustrated by action 1411.

In various embodiments, Receive Wavelet(s) from Fabric 1412 correspondsin various respects to Receive Wavelet at Router 1503 of FIG. 15.

In various embodiments and/or usage scenarios, all or any portions ofany one or more of elements of Wavelet Creation Flow 1400 correspondconceptually to and/or are related conceptually to operations performedby and/or elements of a PE, e.g., PE 499 of FIG. 4.

In various embodiments and/or usage scenarios, all or any portions ofany one or more of elements of Wavelet Creation Flow 1400 (e.g., any oneor more of actions 1403-1409) correspond conceptually to and/or arerelated conceptually to operations performed by and/or elements of acompute element, such as all or any portions of a CE of a PE, e.g.,Compute Element 520 of FIG. 5 and/or CE 800 of FIG. 8. As an example,the destination DSR (associated with Set DSR Destination (Fabric) DSR1404) is one of DSRs 846. In some scenarios, the source DSR (associatedwith Set Source 1403) is one of DSRs 846; in other scenarios the sourceregister (associated with Set Source 1403) is one of RF 842.

As another example, CE 800 as the CE of the transmitting PE performsaction 1403 in response to a load DSR instruction copying informationfrom Memory 854 into the source DSR (e.g., one of DSRs 846). In variousembodiments, the source DSR specifies the location of the data elementsas one of Memory 854, D-Store 848, and RF 842. In some scenarios, thesource DSR specifies an address of a first data element in Memory 854(e.g., address 0x0008), a number of data elements (e.g., nine dataelements), and a stride between subsequent data elements (e.g., 12bytes). As another example, CE 800 performs action 1403 by writing datainto a register of RF 842.

As another example, CE 800 as the CE of the transmitting PE performsaction 1404 in response to a load DSR instruction copying informationfrom Memory 854 into the destination DSR (e.g., one of DSRs 846). Invarious embodiments, the destination DSR specifies transformation of oneor more data elements into one or more wavelets and transmitted byRouter 510 via a fabric-coupled egress port (e.g., North 513). Thedestination DSR specifies a color for the wavelet(s), a control bit forthe wavelet(s), a number of data elements (e.g., length), andinformation about an index of the wavelet(s). In some scenarios, thedestination DSR specifies the value of the index and in other scenariosthe destination DSR specifies a location of the value of the index(e.g., in a register of RF 842).

As another example, CE 800 as the CE of the transmitting PE performsactions 1406, 1407, 1408, and 1409 in response to fetching and decodingan instruction specifying a destination DSR as a destination operand(action 1405). In some embodiments and/or usage scenarios, D-Seq 844reads the source DSR(s) and accesses one, two, or four data elementsspecified by each source DSR, e.g., from Memory 854 or D-Store 848,thereby performing action 1407. In various embodiments, Memory 854and/or D-Store 848 provide the data elements to Data Path 852. The DataPath 852 performs the operation on the data elements (e.g., addingsource0 data elements to source1 data elements). In accordance with thedestination DSD, Data Path 852 transforms the result data of theoperation into a wavelet and writes the wavelet to one of Output Queues859 as specified by a color of the destination DSD, thereby performingaction 1408. In some embodiments, CE 800 of the transmitting PE performsaction 1409 by comparing a number of data elements specified in thedestination DSD (e.g., a length) against the number of data elementssent via action 1408 (e.g., tracked by a counter).

As another example, CE 800 as the CE of the transmitting PE performsaction 1408. The CE transforms the one or two data element(s) into awavelet payload, according to the destination DSD. In some embodimentsand/or usage scenarios, the CE transforms a single data element into awavelet payload formatted in accordance with Sparse Wavelet 1301 of FIG.13A. The single data element is transformed into an instantiation ofSparse Data 1322, an index value specified by the destination DSD istransformed into an instantiation of Index 1321, and a control bit fromthe destination DSD is transformed into an instantiation of Control Bit1320, thereby forming an instantiation of Sparse Wavelet Payload 1302.

As another example, CE 800 as the CE of the transmitting PE transformstwo data elements into a wavelet payload formatted in accordance withDense Wavelet 1331 of FIG. 13B. The first data element is transformedinto an instantiation of Dense Data 1343.1 and the second data elementis transformed into an instantiation of Dense Data 1343.2. The controlbit from the destination DSD is transformed into an instantiation ofControl Bit 1340, thereby forming an instantiation of Dense WaveletPayload 1332.

In some embodiments, the CE provides the wavelet(s) to the routerasynchronously (e.g., in accordance with action 760 of FIG. 7C).

In various embodiments and/or usage scenarios, all or any portions ofany one or more of elements of Wavelet Creation Flow 1400 (e.g., any oneor more of actions 1411 and 1412) correspond conceptually to and/or arerelated conceptually to operations performed by and/or elements of arouter, such as all or any portions of a router of a PE, e.g., Router510 of FIG. 5 and/or Router 600 of FIG. 6, action 760 of FIG. 7C, andaction 747 of FIG. 7B.

As an example, Transmit Wavelet(s) to Fabric 1411 is performed by Router600 as Router of Transmitting PE 1430 in accordance with action 760 ofFIG. 7C. As another example, Receive Wavelet(s) from Fabric 1412 isperformed by Router 600 as Router of Receiving PE 1440 in accordancewith action 747 of FIG. 7B.

In some embodiments and/or usage scenarios, all or any portions ofelements of Wavelet Creation Flow 1400 conceptually correspond to all orany portions of executions of instructions of Task SW on PEs 260 of FIG.2.

FIG. 15 illustrates selected details of an embodiment of receiving awavelet as Wavelet Receive Flow 1500. Actions of Wavelet Receive Flow1500 are performed by various agents. A receiving PE comprises a routerperforming actions 1503-1506, as illustrated by Router of Receiving PE1520. The receiving PE further comprises a CE performing action 1507, asillustrated by CE of Receiving PE 1530.

Receiving a wavelet begins (Start 1501) by initializing at least onetransmitting PE and one or more receiving PEs as well any PEs comprisingrouters implementing fabric coupling the transmitting PEs and thereceiving PEs (Initialize PEs 1502). Each of the PEs comprises arespective router (e.g., Router 510 of FIG. 5) and a respective CE(e.g., Compute Element 520 of FIG. 5). In some scenarios, initializing aPE enables the CE of the PE to perform computations and enables therouter of the PE to transmit, receive, and/or forward wavelets over thefabric.

The following description assumes there is a single receiving PE. Inusage scenarios where there is plurality of receiving PEs, therespective routers and CEs of each of the receiving PEs performprocessing in accordance with FIG. 15.

The router of the receiving PE receives a wavelet ‘on a color’ (e.g.,the wavelet comprises the color) of the fabric (Receive Wavelet atRouter 1503), as transmitted by the transmitting PE. The router checksthe destination(s) of the wavelet based on the color, e.g., by reading aconfiguration register. If the destination(s) of the wavelet includesother PEs (To Other PE(s)? 1504), then the router transmits the waveletto the destination PE(s). The router sends the wavelet to output(s) ofthe router (Transmit Wavelet to Output(s) 1505), and the wavelet istransmitted from the output across the fabric to the destination PE(s).If the destination(s) of the wavelet does not include other PEs, thenthe transmitting is omitted.

If the destination(s) of the wavelet do not include the local CE (ForLocal CE? 1506), then no further action is taken (End 1510). If one ofthe destination(s) of the wavelet is the local CE, then the routerprovides the wavelet to the local CE via the Off Ramp and the wavelet iswritten into a picker queue associated with the color that the waveletwas received on (Write Wavelet to Picker Queue 1507), thereby receivingthe wavelet (End 1510).

In various embodiments and/or usage scenarios, all or any portions ofany one or more of elements of Wavelet Receive Flow 1500 (e.g., any oneor more of actions 1503-1506) correspond conceptually to and/or arerelated conceptually to operations performed by and/or elements of arouter, such as all or any portions of a router of a PE, e.g., Router510 of FIG. 5 and/or Router 600 of FIG. 6.

As an example, Receive Wavelet at Router 1503 is performed by Router 600as Router of Receiving PE 1520 when a wavelet is received on one of DataIn 610. Subsequently, To Other PE(s)? 1504 and For Local CE? 1506 areperformed by Router 600, using the color of the wavelet to determine thedestination(s) of the wavelet, e.g., by reading Dest 661. For each inputcolor, Dest 661 indicates the output destination(s), e.g., one or moreof Data Out 620. If Dest 661 indicates that the output includes otherPEs (e.g., via one of SkipX+ 621, SkipX− 622, X+ 623, X− 624, Y+ 625,and Y− 626), then the wavelet is sent to other PEs by Router Sched 654.If Dest 661 indicates that the output includes the CE of the PE (e.g.,Off Ramp 627), then the wavelet is sent to the CE by Router Sched 654.The wavelet remains in one of Data Queues 650 until action 1505 isperformed by scheduling the wavelet (e.g., by Router Sched 654) to besent to one or more of Data Out 620.

In various embodiments and/or usage scenarios, all or any portions ofany one or more of elements of Wavelet Receive Flow 1500 (e.g., action1507) correspond conceptually to and/or are related conceptually tooperations performed by and/or elements of a compute element, such asall or any portions of a CE of a PE, e.g., Compute Element 520 of FIG. 5and/or CE 800 of FIG. 8. As an example, Write Wavelet to Picker Queue1507 is performed by sending the wavelet via Off Ramp 820 to CE 800 andwriting the wavelet into one of Input Qs 897. In some embodiments,action 1507 additionally comprises setting the active bit (of ActiveBits 898) corresponding to the one of Input Qs 897.

In some embodiments and/or usage scenarios, wavelets are received by therouter, queued, and routed to router output ports without any specificdetermination that a wavelet is for a local CE. Instead, waveletsdestined for the local CE are routed to the off ramp and are thenwritten into the picker queue. Wavelets not destined for the local CEare routed to other-than the off ramp router outputs.

FIG. 16 illustrates selected details of an embodiment of consuming awavelet as Wavelet Consumption Flow 1600. Actions of Wavelet ConsumptionFlow 1600 are performed by a CE of a PE.

Consuming a wavelet begins (Start 1601) by the picker selecting thewavelet from a queue for processing (Picker Selects Wavelet forProcessing 1602), and then the CE processes the wavelet. The CE fetchesand executes instructions associated with the wavelet (Fetch, ExecuteInstructions 1603), thereby consuming the wavelet (End 1604). In someembodiments and/or usage scenarios, fetching and executing instructionsassociated with the wavelet ends with fetching and executing a terminateinstruction.

In some embodiments, Picker Selects Wavelet for Processing 1602 isperformed by Picker 830 of FIG. 8. In various scenarios, Picker 830selects one of Input Qs 897 that is ready (e.g., Block Bits 899 andActive Bits 898 are certain values), according to a scheduling policysuch as round-robin or pick-from-last. In some embodiments, portions ofWavelet Consumption Flow 1600 correspond to portions of Processing aWavelet for Task Initiation 900 of FIG. 9A. As an example, action 1602corresponds to action 902. As another example, action 1603 correspondsto actions 903, 904, 910, 905, and 906.

In some other scenarios, the wavelet is accessed as an operand by aninstruction (e.g., FMACH) executing on the CE and the wavelet isconsumed by the CE during the execution of the instruction, e.g., asillustrated in FIG. 23.

Neuron Smearing

FIG. 17 illustrates selected details of an embodiment of a neuralnetwork as Neural Network 1700. Network 1700 comprises three portionsInput Layer 1710, Internal Layers 1720, and Output Layer 1740. Eachlayer comprises a plurality of neurons. Input Layer 1710, comprisesneurons N11 1711, N12 1712, and N13 1713. Internal Layers 1720 comprisesa first layer of neurons N21 1721, N22 1722, N23 1723, and N24 1724,followed by a second layer of neurons N31 1731, N32 1732, and N33 1733.Output Layer 1740 comprises neurons N41 1741 and N42 1742.

Selected neurons (N21 1721, N22 1722, N23 1723, and N24 1724 as well asN31 1731 and N32 1732) and communications (1791, 1792, and 1793) betweenthe selected neurons are highlighted in the figure. The selected neuronsand pathways are discussed in more detail following.

FIG. 18A illustrates selected details of a first embodiment of anallocation of processing elements to neurons. Sometimes allocation ofprocessing elements to neurons is referred to as placing neurons inprocessing elements or alternatively placement of neurons. Like numberedelements of FIG. 18A correspond to like numbered elements of FIG. 17. Afirst allocation of processing elements to a subset of neurons of FIG.17 (the highlighted neurons N21 1721, N22 1722, N23 1723, and N24 1724as well as N31 1731 and N32 1732) is conceptually illustrated. Verticaldistance in the figure indicates relative usage of computationalresources of each of five processing elements PE0 1820, PE1 1821, PE21822, PE3 1823, PE4 1824, and PE5 1825.

Each of neurons N21 1721, N22 1722, N23 1723, and N24 1724 representsapproximately an equal amount of computational resources, e.g., Moperations, K storage capacity, and J bandwidth to and from the storage.Each of neurons N31 1731 and N32 1732 represents approximately an equalamount of computational resources, e.g., M/2 operations, K/2 storage,and J/2 bandwidth. Thus, each of N31 1731 and N32 1732 representsapproximately one half the computational resources of each of N21 1721,N22 1722, N23 1723, and N24 1724. In various embodiments, examples ofcomputational resources comprise compute operations, storage capacity,read bandwidth from storage, write bandwidth to storage, inputconnections from other neurons, and output connections to other neurons.

In the illustrated embodiment, neuron processing is allocated such thateach of the foregoing neurons is allocated to an entire PE. Morespecifically, N21 1721 is allocated to PE0 1820, N22 1722 is allocatedto PE1 1821, N23 1723 is allocated to PE2 1822, N24 1724 is allocated toPE3 1823, N31 1731 is allocated to PE4 1824, and N32 1732 is allocatedto PE5 1825. Therefore, four of the six processing elements are fullysubscribed (PE0 1820, PE1 1821, PE2 1822, and PE3 1823), while two ofthe six processing elements are only one-half subscribed (PE4 1824 andPE5 1825).

FIG. 18B illustrates selected details of a second embodiment of anallocation of processing elements to neurons. Like numbered elements ofFIG. 18B correspond to like numbered elements of FIG. 17 and FIG. 18A. Asecond allocation of processing elements to a subset of neurons of FIG.17 (the highlighted neurons N21 1721, N22 1722, N23 1723, and N24 1724as well as N31 1731 and N32 1732) is conceptually illustrated. As inFIG. 18A, vertical distance in the figure indicates relative usage ofcomputational resources of each of five processing elements PE0 1820,PE1 1821, PE2 1822, PE3 1823, PE4 1824, and PE5 1825. Also, as in FIG.18A, each of N31 1731 and N32 1732 represents approximately one half thecomputational resources of each of N21 1721, N22 1722, N23 1723, and N241724.

In the illustrated embodiment, neuron processing is allocated such thatprocessing for respective neurons is “smeared” across processingelements. Conceptually, neurons are “split” into portions suitable forprocessing elements to be allocated to. As illustrated in the figure,neurons are split and processing elements allocated so that four of thesix processing elements are equally (and fully) subscribed (PE0 1820,PE1 1821, PE2 1822, and PE3 1823), while two of the six processingelements are completely unsubscribed and therefore available for otheruses (PE4 1824, and PE5 1825). In some embodiments and/or usagescenarios, unsubscribed processing elements remain unused and consumelittle or no active and/or static power (e.g., via one or more of clockgating and power gating). More specifically, N21 1721 is allocated intwo halves (½ N21 1721.1 and ½ N21 1721.2) to two respective processingelements (PE0 1820 and PE2 1822). Similarly, N22 1722 is allocated intwo halves (½ N22 1722.1 and ½ N22 1722.2) to two respective processingelements (PE0 1820 and PE2 1822). N23 1723 is allocated in two halves (½N23 1723.1 and ½ N23 1723.2) to two respective processing elements (PE11821 and PE3 1823) and N24 1724 is allocated in two halves (½ N24 1724.1and ½ N24 1724.2) to two respective processing elements (PE1 1821 andPE3 1823). N31 1731 is allocated in four fourths (¼ N31 1731.1, ¼ N311731.2, ¼ N31 1731.3, and ¼ N31 1731.4) to four respective processingelements (PE0 1820, PE1 1821, PE2 1822, and PE3 1823). Similarly, N321732 is allocated in four fourths (¼ N32 1732.1, ¼ N32 1732.2, ¼ N321732.3, and ¼ N32 1732.4) to four respective processing elements (PE01820, PE1 1821, PE2 1822, and PE3 1823). In various embodiments, neuronsare split, and processing elements allocated based on one or morecomputational resources associated with the neurons. In someembodiments, neurons are split, and processing elements allocated basedon the hardware resources available in the processing elements (e.g.,some neurons require specific hardware resources such as PRNGs).

FIG. 19 illustrates selected details of an embodiment of smearing aneuron across a plurality of processing elements. The splitting resultsin portions of the split neuron that are then smeared across processingelements. Like numbered elements of FIG. 19 correspond to like numberedelements of FIG. 17, FIG. 18A, and FIG. 18B. As illustrated by FIG. 18B,N21 1721 is split into two portions ½ N21 1721.1 and ½ N21 1721.2implemented respectively by PE0 1820 and PE2 1822.

Conceptually, N21 1721 is considered to comprise local compute and localstorage, as well as inputs and outputs. Respective elements of N21 1721are partitioned respectively. The local compute of N21 is partitionedinto ½ Local Compute 1930.1 and ½ Local Compute 1930.2. The localstorage of N21 is partitioned into ½ Local Storage 1940.1 and ½ LocalStorage 1940.2. The inputs of N21 are partitioned into a first half in01910, in1 1911 and in2 1912 as well as a second half in3 1913, in4 1914,and in5 1915. The outputs of N21 are partitioned into a first half out01920, out1 1921, out2 1922 as well as a second half out3 1923, out41924, and out5 1925.

½ Local Compute 1930.1, ½ Local Storage 1940.1, in0 1910, inl 1911, in21912, out0 1920, out1 1921, and out2 1922 are implemented by PE0 1820. ½Local Compute 1930.2, ½ Local Storage 1940.2, in3 1913, in4 1914, andin5 1915, out3 1923, out4 1924, and out5 1925 are implemented by PE21822.

In some embodiments and/or usage scenarios, smearing a neuron acrossmore than one processing element comprises combining partial resultsfrom the portions of the smeared neuron into results corresponding toresults of the entire (original non-smeared) neuron. The combining isimplemented, e.g., at least in part by additional computation,additional storage, and/or additional communication that would nototherwise be performed/used by the entire neuron. Additional Compute1950.1 and Additional Storage 1960.1 are representative of additionalcompute and additional storage for ½ N21 1721.1, and are implemented byPE0 1820. Additional Compute 1950.2 and Additional Storage 1960.2 arerepresentative of additional compute and additional storage for ½ N211721.2, and are implemented by PE2 1822.

Additional Communication 1970 is representative of additionalcommunication between ½ N21 1721.1 and ½ N21 1721.2, and is implementedby fabric connectivity between PE0 1820 and PE2 1822. In someembodiments and/or usage scenarios, all or any portions of AdditionalCommunication 1970 is representative of communications that would occurinternally to a single processing element if the single processingelement entirely implemented N21 1721.

FIG. 20 illustrates selected details of an embodiment of communicationbetween portions of split neurons. Like numbered elements of FIG. 20correspond to like numbered elements of FIG. 17, FIG. 18A, FIG. 18B, andFIG. 19. Allocations of PE0 1820, PE1 1821, PE2 1822, and PE3 1823 toneuron portions are as illustrated by FIG. 18B. For clarity, onlyallocations specific to PE0 1820 and PE1 1821 are illustrated.

Wafer Portion 2000 comprises PE0 1820, PE1 1821, PE2 1822, and PE3 1823.Couplings between PEs of Wafer Portion 2000 are illustrated as (couplingbetween adjacent PEs) 2040 coupling PE0 1820 and PE1 1821, 2041 couplingPE1 1821 and PE3 1823, 2043 coupling PE3 1823 and PE2 1822, and 2044coupling PE2 1822 and PE0 1820. Couplings to PEs adjacent to WaferPortion 2000 are illustrated as (portion of coupling between adjacentPEs) 2050, 2051, 2052, 2053, 2054, 2055, 2056, and 2057. The couplingsto adjacent PEs are ‘portions’ since in some embodiments and/or usagescenarios, all or any portions of the couplings are comprised in waferportions adjacent to Wafer Portion 2000, rather than entirely in WaferPortion 2000. In various embodiments and/or usage scenarios, and as atleast in part further described elsewhere herein, communication betweenprocessing elements over the couplings is via virtual channel, a type oflogical coupling implemented by the routers within the processingelements, in accordance with a specified color of a wavelet, e.g., asdetermined by Neuron to PE Mapping SW 212 of FIG. 2 executing onPlacement Server(s) 150 of FIG. 1. It is understood that a wavelet is atype of packet (a network packet), “fabric packet” refers to a packetthat is fabric-transfer-enabled (enabled for and compatible withphysical transfer over physical fabric couplings), “fabric vector”refers to fabric-transfer-enabled vector data, and the neuron smearingconcepts herein (including but not limited to communication via virtualchannels) apply to embodiments described in terms of communications,computations, or storage, using packets, fabric packets, or fabricvectors.

As a first example, communication portion 1791.1 conceptually representsa portion of communication 1791 between N11 1711 and N21 1721 (of FIG.17), e.g., from an input layer to an internal layer, with portions of asplit neuron in respective processing elements. More specifically,recall that N21 1721 is split into two portions (½ N21 1721.1 and ½ N211721.2; see FIG. 18B). Thus, communication 1791 is split into twoportions. Communication portion 1791.1 is illustrative specifically ofthe portion that is with respect to ½ N21 1721.1. Communication portion1791.1 is transported via (portion of coupling between adjacent PEs)2057 between a PE adjacent to Wafer Portion 2000 to PE0 1820 (allocatedto ½ N21 1721.1). In some embodiments and/or usage scenarios,communication 1791 is split into two portions, communication portion1791.1 (illustrated) and communication portion 1791.2 (not illustrated).In some embodiments and/or usage scenarios, transport of communicationportion 1791.1 and communication portion 1791.2 are via a same virtualchannel In some embodiments and/or usage scenarios, transport ofcommunication portion 1791.1 and communication portion 1791.2 are viarespective unique virtual channels.

As a second example, communication portion 1792.1 conceptuallyrepresents a portion of communication 1792 between N21 1721 and N31 1731(of FIG. 17), e.g., from a first internal layer to a second internallayer, with portions of split neurons in respective processing elements.More specifically, recall that N21 1721 is split into two portions (½N21 1721.1 and ½ N21 1721.2; see FIG. 18B). Further recall that N31 1731is split into four portions (¼ N31 1731.1, ¼ N31 1731.2, ¼ N31 1731.3,and ¼ N31 1731.4; see FIG. 18B). Thus, communication 1792 is split intoportions. Communication portion 1792.1 is illustrative specifically ofthe portion that is with respect to ½ N21 1721.1 and ¼ N31 1731.2.Communication portion 1792.1 is transported via (coupling betweenadjacent PEs) 2040 between PE0 1820 (allocated to ½ N21 1721.1) and PE11821 (allocated to ¼ N31 1731.2). In various embodiments and/or usagescenarios, transport of communication portion 1792.1 (illustrated) and,e.g., other portions (not illustrated) of communication 1792 are via asame virtual channel, via unique virtual channels per portion, viavirtual channels per portion associated with a particular neuron, and/orvia virtual channels per portion associated with a particular processingelement.

As a third example, communication portion 1793.1 conceptually representsa portion of communication 1793 between N23 1723 and N31 1731 (of FIG.17), e.g., from a first internal layer to a second internal layer, withportions of split neurons in a same processing element. Morespecifically, recall that N23 1723 is split into two portions (½ N231723.1 and ½ N23 1723.2); see FIG. 18B). Further recall that N31 1731 issplit into four portions (¼ N31 1731.1, ¼ N31 1731.2, ¼ N31 1731.3, and¼ N31 1731.4; see FIG. 18B). Thus, communication 1793 is split intoportions. Communication portion 1793.1 is illustrative specifically ofthe portion that is with respect to ½ N23 1723.1 and ¼ N31 1731.2.Communication portion 1793.1 is transported via one or more mechanismsinternal to PE1 1821 (allocated to ½ N23 1723.1 and ¼ N31 1731.2). E.g.,PE1 1821 uses internal resources (such as a router) to internallyfeedback an output as an input, and/or to internally provide an inputfrom an output. In some embodiments and/or usage scenarios, transport ofcommunication portion 1793.1 is via a virtual channel that results in anoutput being used as an input, and/or an input being provided from anoutput.

As a fourth example, communication 2060 conceptually represents all orany portions of Additional Communication 1970 (of FIG. 19), e.g.,communications within a neuron that is split across processing elements.More specifically, communication 2060 illustrates specificallycommunications between two of the four portions that N32 1732 is splitinto (¼ N32 1732.1 and ¼ N32 1732.2; see FIG. 18B). Communication 2060is transported via (coupling between adjacent PEs) 2040 between PE0 1820(allocated to ¼ N32 1732.1) and PE1 1821 (allocated to ¼ N32 1732.2). Invarious embodiments and/or usage scenarios, communication 2060 is viavirtual channel dedicated to communication 2060, a virtual channelshared with communication 2060 and communications between other portionsof N32 1732, and a virtual channel shared with communication 2060 andall or any portions of neurons split across processing elements.

In some embodiments and/or usage scenarios, all or any portion of WaferPortion 2000 comprises PEs 122 of FIG. 1. In some embodiments and/orusage scenarios, any one of PE0 1820, PE1 1821, PE2 1822, and PE3 1823correspond to PE 497 of FIG. 4. In some embodiments and/or usagescenarios, any one or more of coupling between adjacent PEs 2041, 2040,2043, and 2044 and/or portion of coupling between adjacent PEs 2050,2051, 2052, 2053, 2054, 2055, 2056, and 2057 correspond to any one ormore of North coupling 430, East coupling 431, South coupling 432, andWest coupling 433 of FIG. 4.

Concepts relating to neuron smearing (e.g., as described with respect toand illustrated by FIG. 17, FIG. 18A, FIG. 18B, FIG. 19, and FIG. 20)are applicable to neural networks of various topologies and types, suchas FCNNs, RNNs, CNNs, LSTM networks, autoencoders, deep belief networks,and generative adversarial networks.

In various embodiments and/or usage scenarios, neurons are split intosame-sized portions, e.g., halves, fourths, eights, and so forth. Invarious embodiments and/or usage scenarios, neurons are split intodifferent-sized portions, e.g., a first portion that is a half, andsecond and third portions that are respectively each fourths. In variousembodiments and/or usage scenarios, neurons are split intoarbitrarily-sized portions.

In various embodiments and/or usage scenarios, a multiplicity of PEs isallocated to a single neuron. In various embodiments and/or usagescenarios, a single PE is allocated to the respective entireties of amultiplicity of neurons.

In various embodiments and/or usage scenarios, allocation of PEs toneurons is entirely or partially responsive to static and/or dynamicmeasurements of computational and/or storage requirements. In variousembodiments and/or usage scenarios, allocation of PEs to neurons isentirely or partially responsive to dimensionality of data to beprocessed.

In various embodiments and/or usage scenarios, dataflow as representedby directions of arrows is unidirectional (as illustrated by drawnarrowhead), bidirectional, and/or reverse-direction (against drawnarrowhead). As a specific example, in various embodiments and/or usagescenarios, communication 1792 (of FIG. 17) is representative of dataflowfrom N21 1721 to N31 1731 (e.g., during forward propagation) or inreverse from N31 1731 to N21 1721 (e.g., during back propagation). Thus,communication portion 1792.1 and therefore communication on (portion ofcoupling between adjacent PEs) 2040 occurs from PE0 1820 to PE1 1821(e.g., during forward propagation) and in reverse from PE1 1821 to PE01820 (e.g., during back propagation).

In various embodiments and/or usage scenarios, each neuron has:associated storage for a weight per incoming activation, a partial sumaccumulation computation, and an output activation function computation.For those scenarios in which single neurons are split across multiplePEs, the weights are respectively locally stored in the multiple PEs,multiply and accumulate operations are respectively locally performed inthe multiple PEs, and locally generated partial sums are communicatedvia virtual channels to a particular PE for production of a final sum.The activation function following the final sum can be performed in thesame particular PE or in another PE, all as determined by Neuron to PEMapping SW 212 of FIG. 2 executing on Placement Server(s) 150 of FIG. 1.Non-zero activation outputs are communicated via virtual channels toneurons of a subsequent layer of the neural network.

In various embodiments and/or usage scenarios, the partial sums, theaccumulations, and the activation functions, are implemented using alldigital techniques, including digital logic and/or digital processing.In various embodiments and/or usage scenarios, exclusive of defects, thefabric comprises a homogenous collection of PEs enabled to performdigital arithmetic via one or more of: a task performing floating-pointarithmetic, floating-point multiplier logic, fused multiply andaccumulate digital logic, and floating-point addition using stochasticrounding. In various embodiments and/or usage scenarios, the PEs of thehomogenous collection are further enabled to perform each activationfunctions as a nonlinear activation function selected from the groupconsisting of Rectified Linear Unit (ReLU), sigmoid, and tan h.

It is understood that the representation in FIG. 17 of a neural networkis a type of dataflow graph, and the foregoing concepts relating toneural networks and neuron smearing apply to embodiments described interms of a dataflow graph. In some embodiments and/or usage scenarios,nodes of the dataflow graph correspond to neurons, node slicescorrespond to split neurons, and one or more of the nodes areimplemented using resources of a plurality of processing elements.

Vectors and Data Structure Descriptors

In various embodiments and/or usage scenarios, processing of one or morevectors, each vector comprising respective one or more of data elements,is performed. A vector is variously read from memory (e.g., of a CE of aPE, such as Memory 854 or D-Store 848 of FIG. 8), written to the memory,received from a fabric, or transmitted to the fabric. Vectors read fromor written to the memory are sometimes referred to as ‘memory vectors’.Vectors received from or transmitted to the fabric (e.g., as wavelets)are sometimes referred to as ‘fabric vectors’. DSDs from DSRs (as wellas XDXDs from XDSRs) are usable to determine addressing patterns formemory vectors and accessing patterns for fabric vectors.

Each element identifier in the description of FIGS. 21A-E, FIGS. 22A-B,and FIGS. 23-24 having a first digit of “8” refers to an element of FIG.8, and for brevity is not otherwise specifically identified as being anelement of FIG. 8.

FIG. 21A illustrates selected details of an embodiment of a Fabric InputData Structure Descriptor (aka Fabric Input DSD), as Fabric Input DataStructure Descriptor 2100. In some embodiments, Fabric Input DataStructure Descriptor 2100 describes a fabric vector received by a PEfrom the fabric, as well as various parameters relating to processing ofthe fabric vector. In various embodiments and/or usage scenarios, eithera source0 operand or a source1 operand of an instruction refers to a DSRcontaining an instance of a DSD in accordance with Fabric Input DataStructure Descriptor 2100.

Fabric Input Data Structure Descriptor 2100 comprises Length 2101, UTID(Microthread Identifier) 2102, UE (Microthread Enable) 2103, SW (SIMDWidth) 2104, AC (Activate Color) 2105, Term (Terminate Microthread onControl Wavelet) 2106, CX (Control Wavelet Transform Enable) 2107, US(Microthread Sparse Mode) 2108, Type 2109, SS (Single Step) 2110, SA(Save Address/Conditional Single Step Mode) 2111, SC (ColorSpecified/Normal Mode) 2112, SQ (Queue Specified/Normal Mode) 2113, andCH (Color High) 2114.

In some embodiments, Length 2101 comprises a 15-bit integer specifyingthe length of the vector, e.g., the number of data elements in thevector.

In some embodiments, UE (Microthread Enable) 2103 comprises a 1-bitfield indicating whether, under at least some conditions, microthreadingis enabled during processing of the fabric vector, sometimes referred toas the fabric vector ‘enabling microthreading’. If at least one operand(source or destination) of an instruction is a fabric vector enablingmicrothreading, then the instruction is referred to as a ‘microthreadedinstruction’, and on either an input or output stall during processingan iteration of the instruction, processing is enabled to proceed(provided sufficient microthreading resource are available) to anotherinstruction (e.g., of the same task, or of another task). When the stallis cleared, then processing (eventually) returns to the previouslystalled instruction at the iteration that was stalled. An example inputstall is when at least one element of an input fabric vector or a FIFOoperand is not available as an input (e.g., a source data element). Anexample output stall is when there is insufficient space to bufferresults associated with an element of an output fabric vector or a FIFOfor an output (e.g., a destination data element). In some scenarios, afabric vector that does not enable microthreading is processedsynchronously and stalls processing on either an input or output stall.In some scenarios, a fabric vector that enables microthreading isprocessed asynchronously and reduces or avoids stalling the processingelement on either an input or output stall. If a fabric vector enablesmicrothreading, then the processing element is enabled to conditionallyswitch to processing a different instruction (instead of stalling) andsubsequently resume processing the fabric vector at a later point intime (e.g., when data is available).

In some embodiments, UTID (Microthread Identifier) 2102 comprises a3-bit field identifying one of a plurality of microthreads and/orresources associated with one of a plurality of microthreads. Themicrothreads and/or the resources are associated, e.g., with a fabricvector that enables microthreading. In some embodiments, the hardwareprovides resources for eight microthreads. In some embodiments and/orusage scenarios, UTID 2102 identifies or partially identifies one ofInput Qs 897.

In some embodiments, SW (SIMD Width) 2104 comprises a 2-bit fieldspecifying the number of operations (e.g., one, two, or four) that are,in some implementations, executed in parallel. For example, an FMACH,FADDH, FMULH or MOV16 instruction performs multiple (up to four)operations in parallel on respective operands. In some implementation,the SW field is used to determine how to parse wavelets into data versusindex information. For example, when the SW field is four, then twowavelets, each having two data values (and no index values) provide fouroperands, e.g., in parallel. Continuing with the example, when the SWfield is two, then a single wavelet having two data values (and no indexvalue) provides two operands, e.g., in parallel. Continuing with theexample, when the SW field is one, then a single wavelet having a singledata value and a single index value provides a single operand.

In some embodiments, AC (Activate Color) 2105 comprises a 6-bit fieldspecifying a color to activate (e.g., via an activate operation). Insome scenarios, when processing is complete for a fabric vector thatenables microthreading, the color specified by the AC field is activatedand a task initiated based on the activated color. The completion ofprocessing occurs, e.g., when all elements of the fabric vector havebeen processed, or when Term 2106 indicates to terminate uponencountering a control wavelet and a control wavelet is encounteredwhile processing the fabric vector. In some embodiments, AC 2105 isenabled to specify one of: a local color and a fabric color.

In some embodiments, Term (Terminate Microthread on Control Wavelet)2106 comprises a 1-bit field specifying whether to terminate uponreceiving a control wavelet. If the wavelet at the head of the queuespecified by Fabric Input Data Structure Descriptor 2100 (e.g., one ofInput Qs 897 as variously specified by various functions of anycombination of UTID 2102, SC 2112, and/or SQ 2113, as describedelsewhere herein) is a control wavelet (e.g., Control Bit 1320 of FIG.13A or Control Bit 1340 of FIG. 13B is asserted) and Term 2106 isasserted, then the instruction is terminated and the color specified byAC 2105 is activated.

In some embodiments, CX (Control Wavelet Transform Enable) 2107comprises a 1-bit field specifying whether to transform controlwavelets. If CX 2107 is asserted, then in response to receiving acontrol wavelet in the fabric vector, bits 15:6 of the index registerare all ‘1’s. In some embodiments and/or usage scenarios, if bits 15:6of the index register are all ‘1’s, then the control bits of any outputwavelets associated with an output fabric vector referencing the indexregister are asserted.

In some embodiments, US (Microthread Sparse Mode) 2108 comprises a 1-bitfield specifying whether a fabric vector that enables microthreading(e.g., via the UE field) is processed in a sparse mode. If US 2108 isasserted, then the fabric vector comprises a vector of sparse dataelements and respective wavelet indices of the operand described byFabric Input Data Structure Descriptor 2100. The indices are optionallyand/or selectively used for address calculation of memory operands,dependent on WLI 2152 (of FIG. 21C).

In some embodiments, Type 2109 comprises a 3-bit field specifying a datastructure type and/or how to interpret other fields of Fabric Input DataStructure Descriptor 2100. Type 2109 is “0” for all instances of FabricInput Data Structure Descriptor 2100.

In some embodiments, SS (Single Step) 2110 comprises a 1-bit fieldspecifying whether single step mode operation is enabled, under at leastsome conditions, for operations using the DSD as an operand. In somescenarios, an instruction with one or more operands that enable singlestep mode operates in single step mode.

In some embodiments, SA (Save Address/Conditional Single Step Mode) 2111comprises a 1-bit field specifying whether save address mode operationis enabled, under at least some conditions, for operations using the DSDas an operand.

In some embodiments and/or usage scenarios, a color is activated and inresponse a task is initiated at an address based at least in part on thecolor. Once initiated, the task executes. In some scenarios, an inputfabric vector is provided from the queue associated with the color ofthe currently executing task. In some embodiments, SC (Color Specified,Normal Mode) 2112 comprises a 1-bit field that if asserted, specifiesthat the input fabric vector is provided from a specific queue (e.g.,one of Input Qs 897) associated with a specific fabric color. Thespecific fabric color is specified (e.g., as a 5-bit color) as aconcatenation of lower bits UTID 2102 (comprising a 3-bit field) andupper bits CH 2114 (comprising a 2-bit field). In some embodiments, SQ(Queue Specified, Normal Mode) 2113 comprises a 1-bit field that ifasserted, specifies that the input fabric vector is provided from aspecific queue (e.g., one of Input Qs 897). If SQ 2113 is asserted, thenthe input fabric vector is provided from the one of Input Qs 897specified by UTID 2102.

FIG. 21B illustrates selected details of an embodiment of a FabricOutput Data Structure Descriptor (aka Fabric Output DSD), as FabricOutput Data Structure Descriptor 2120. In some embodiments, FabricOutput Data Structure Descriptor 2120 describes a fabric vector createdby a PE and transmitted over the fabric, as well as various parametersrelating to processing of the fabric vector. In various embodimentsand/or usage scenarios, a destination operand of an instruction refersto a DSR containing an instance of a DSD in accordance with FabricOutput Data Structure Descriptor 2120.

Fabric Output Data Structure Descriptor 2120 comprises Length 2121, UTID(Microthread Identifier) 2122, UE (Microthread Enable) 2123, SW (SIMDWidth) 2124, Color 2126, C (Output Control Bit) 2127, Index Low 2128.1,Type 2129, SS (Single Step) 2130, SA (Save Address/Conditional SingleStep Mode) 2131, WLI (Wavelet Index Select) 2132, Index High 2128.2, andAC (Activate Color) 2125.

In some embodiments, the elements of Fabric Output Data StructureDescriptor 2120 (Length 2121, UTID 2122, UE 2123, SW 2124, SS 2130, SA2131, and AC 2125) are respectively similar in function and/or operationwith respect to the elements of Fabric input Data Structure Descriptor2100 (Length 2101, UTID 2102, UE 2103, SW 2104, SS 2110, SA 2111, and AC2105).

In some embodiments, Color 2126 comprises a 5-bit field specifying thefabric color used to transmit wavelets associated with the fabricvector.

In some embodiments, C (Output Control Bit) 2127 comprises a 1-bit fieldspecifying whether a wavelet is a control wavelet. If C 2127 isasserted, then any wavelets created based on the DSD are controlwavelets (e.g., Control Bit 1320 of FIG. 13A is asserted).

In some embodiments, Index Low 2128.1 comprises a 3-bit field and IndexHigh 2128.2 comprises a 3-bit field. The concatenation of Index Low2128.1 and Index High 2128.2 is collectively referred to as Index 2128.In some scenarios, Index 2128 is used to form an index for a wavelet(e.g., Index 1321 of FIG. 13A).

In some embodiments, Type 2129 comprises a 3-bit field specifying a datastructure type and/or how to interpret other fields of Fabric OutputData Structure Descriptor 2120. Type 2129 is “0” for all instances ofFabric Output Data Structure Descriptor 2120.

In some embodiments, WLI (Wavelet Index Select) 2132 comprises a 1-bitfield specifying in part the index of the fabric vector. In somescenarios, if WLI 2132 is “1”, then the index is the value from aregister (e.g., GPR4 of RF 842). In some scenarios, if WLI 2132 is “0”,then the index is a zero-extension to 16 bits of Index 2128.

FIG. 21C illustrates selected details of an embodiment of a 1D MemoryVector Data Structure Descriptor (aka 1D Memory Vector DSD), as 1DMemory Vector Data Structure Descriptor 2140. In some embodiments, 1DMemory Vector Data Structure Descriptor 2140 describes a one-dimensionalmemory vector stored in the memory, as well as various parametersrelating to processing of the memory vector. In various embodimentsand/or usage scenarios, any one or more of a source0 operand, a source1operand, and a destination operand of an instruction refer to respectiveDSRs containing respective instances of DSDs in accordance with 1DMemory Vector Data Structure Descriptor 2140.

1D Memory Vector Data Structure Descriptor 2140 comprises Length 2141,Base Address 2142, Type 2149, SS (Single Step) 2150, SA (SaveAddress/Conditional Single Step Mode) 2151, WLI (Wavelet Index Select)2152, and Stride 2153.

In some embodiments, some of the elements of 1D Memory Vector DataStructure Descriptor 2140 (Length 2141, SS 2150, and SA 2151) arerespectively similar in function and/or operation with respect to someof the elements of Fabric Input Data Structure Descriptor 2100 (Length2101, SS 2110, and SA 2111). In some scenarios, if the length of thememory vector is more than 15 bits, then 4D Memory Vector Data StructureDescriptor 2140 is used.

In some embodiments, Base Address 2142 comprises a 15-bit integerspecifying the base address of the memory vector.

In some embodiments, Type 2149 comprises a 3-bit field specifying a datastructure type and/or how to interpret other fields of 1D Memory VectorData Structure Descriptor 2140. Type 2149 is “1” for all instances of 1DMemory Vector Data Structure Descriptor 2140.

In some embodiments, WLI (Wavelet Index Select) 2152 comprises a 1-bitfield specifying in part the index of the vector. If WLI 2152 is “0”,then the index is 0. In some scenarios, if WLI 2152 is “1”, then theindex is the value from a register (e.g., GPR4 of RF 842) or the indexof a sparse wavelet (e.g., Index 1321 of FIG. 13A).

In some embodiments, Stride 2153 comprises a 9-bit signed integerspecifying the stride of the vector. In some scenarios, Base Address2142, an index specified by WLI 2153, and Stride 2153 enable calculatingaddresses of data elements in a 1D memory vector. The address of thefirst data element in the 1D memory vector is Base Address 2142 plus theindex specified by WLI 2153. The address of the next data element in the1D vector is the address of the first data element plus Stride 2153. Forexample, Base Address 2142 is 136, WLI 2153 is 1, GPR4 holds the value6, Stride 2153 is −2, and Length 2141 is 10, then the memory vectorcomprises data located at addresses {142, 140, 138, . . . , 124}. Insome scenarios, if the stride of the memory vector is more than ninebits, then 4D Memory Vector Data Structure Descriptor 2140 is used.

FIG. 21D illustrates selected details of an embodiment of a 4D MemoryVector Data Structure Descriptor (aka 4D Memory Vector DSD), as 4DMemory Vector Data Structure Descriptor 2160. In some embodiments, 4DMemory Vector Data Structure Descriptor 2160, in conjunction with 4DMemory Vector Extended Data Structure Descriptor 2240 of FIG. 22B,describe a 4-dimensional memory vector stored in the memory, as well asvarious parameters relating to processing of the memory vector. In someembodiments, 4D Memory Vector Data Structure Descriptor 2160, inconjunction with 4D Memory Vector Extended Data Structure Descriptor2240 of FIG. 22B, describe a two-dimensional or three-dimensional memoryvector stored in the memory, as well as various parameters relating toprocessing of the memory vector. In various embodiments and/or usagescenarios, any one or more of a source0 operand, a source1 operand, anda destination operand of an instruction refer to respective DSRscontaining respective instances of DSDs in accordance with 4D MemoryVector Data Structure Descriptor 2160.

4D Memory Vector Data Structure Descriptor 2160 comprises Length LowerBits 2161.1, Base Address 2162, Type 2169, SS (Single Step) 2170, SA(Save Address/Conditional Single Step Mode) 2171, WLI (Wavelet IndexSelect) 2172, and Length Upper Bits 2161.2.

In some embodiments, some of the elements of 4D Memory Vector DataStructure Descriptor 2160 (Base Address 2162, SS 2170, SA 2171, and WLI2172) are respectively similar in function and/or operation with respectto 1D Memory Vector Data Structure Descriptor 2140 (Base Address 2142,SS 2150, SA 2151, and WLI 2152).

In some embodiments, Lower Bits 2161.1 comprises a 15-bit field andLength Upper Bits 2161.2 comprises a 9-bit field. The concatenation ofLower Bits 2161.1 and Length Upper Bits 2161.2 is collectively referredto (and illustrated as) Length 2161 (a 24-bit field) interpreted inconjunction with 4D Memory Vector Extended Data Structure Descriptor2240.

In some embodiments, Type 2169 comprises a 3-bit field specifying anextended DSR (XDSR), storing, e.g., an extended DSD (XDSD). The XDSDspecifies and describes one of: a circular memory buffer (e.g., CircularMemory Buffer Extended Data Structure Descriptor 2210 of FIG. 22A) and afour-dimensional memory vector (e.g., 4D Memory Vector Extended DataStructure Descriptor 2240 of FIG. 22B).

FIG. 21E illustrates selected details of an embodiment of a CircularMemory Buffer Data Structure Descriptor (aka Circular Memory BufferDSD), as Circular Memory Buffer Data Structure Descriptor 2180. In someembodiments, Circular Memory Buffer Data Structure Descriptor 2180, inconjunction with Circular Memory Buffer Extended Data StructureDescriptor 2210, describes one of: a circular buffer of data elementsstored in the memory and a FIFO of data elements stored in the memory;as well as various parameters relating to processing of the dataelements. In various embodiments and/or usage scenarios, any one or moreof a source0 operand, a source1 operand, and a destination operand of aninstruction refer to respective DSRs containing respective instances ofDSDs in accordance with Circular Memory Buffer Data Structure Descriptor2180.

Circular Memory Buffer Data Structure Descriptor 2180 comprises Length2181, Base Address 2182, FW (FIFO Wrap Bit) 2188, Type 2189, SS (SingleStep) 2190, SA (Save Address/Conditional Single Step Mode) 2191, WLI(Wavelet Index Select) 2192, and SW (SIMD Width) 2184. In someembodiments, a circular memory buffer access always has an index of zeroand a stride of one.

In some embodiments, some of the elements of Circular Memory Buffer DataStructure Descriptor 2180 (Length 2181, Base Address 2182, SS 2190, andSA 2191) are respectively similar in function and/or operation withrespect to some of the elements of 1D Memory Vector Data StructureDescriptor 2140 (Length 2141, Base Address 2142, SS 2150, and SA 2151).In some embodiments, Type 2189 is similar in function and/or operationto Type 2169 of 4D Memory Vector Data Structure Descriptor 2160. In someembodiments, SW 2184 of Circular Memory Buffer Data Structure Descriptor2180 is similar in function and/or operation to SW 2104 of Fabric InputData Structure Descriptor 2100.

In some embodiments, FW (FIFO Wrap Bit) 2188 comprises a 1-bit fieldenabling distinguishing between a full FIFO and an empty FIFO. FW (FIFOWrap Bit) 2188 is toggled when an access wraps around the address rangeof the FIFO.

In some embodiments, WLI 2192 has no impact on the index of a circularbuffer.

FIG. 22A illustrates selected details of an embodiment of a CircularMemory Buffer Extended Data Structure Descriptor, as Circular MemoryBuffer Extended Data Structure Descriptor 2210. Circular Memory BufferExtended Data Structure Descriptor 2210 comprises Type 2211, StartAddress 2212, End Address 2213, FIFO 2214, Push (Activate) Color 2215,and Pop (Activate) Color 2216.

In some embodiments, Type 2211 comprises a 1-bit field specifying thetype of data structure. Type 2211 is “1” for all instances of CircularMemory Buffer Extended Data Structure Descriptor 2210.

In some embodiments, Start Address 2212 comprises a 15-bit fieldspecifying the start address of the circular buffer in the memory. Insome embodiments, End Address 2213 comprises a 15-bit integer specifyingthe end address of the circular buffer in the memory. When an address isincremented (e.g., by the stride to initiate the next access) and equalsEnd Address 2213, the address is reset to Base Address 2212, therebyproviding circular access behavior.

In some embodiments, FIFO 2214 comprises a 1-bit field specifyingwhether the circular buffer is a FIFO. If FIFO 2214 is “0”, then thecircular buffer is not a FIFO. If FIFO 2214 is “1”, then the circularbuffer is a FIFO.

In some embodiments, Push (Activate) Color 2215 and Pop (Activate) Color2216 comprise 6-bit fields specifying colors to activate (e.g., via anactivate operation). In some embodiments, Push (Activate) Color 2215 andPop (Activate) Color 2216 are enabled to specify ones of: a local colorand a fabric color.

In various embodiments, two circular memory buffer DSRs are enabled todescribe a FIFO of data elements stored in a same region of the memory.A destination DSR (e.g., DDSR8) describes a write pointer of the FIFO,and a source1 DSR (e.g., S1DSR8) describes a read pointer of the FIFO.In some embodiments, destination and source1 DSRs have a sameidentifier. In various embodiments, only some of DSRs 846 are enabled todescribe FIFOs, (e.g., DDSR8-DDSR11 and S1DSR8-S1DSR11).

FW (FIFO Wrap Bit) 2188 of the two DSRs enables detecting if a FIFO isfull or empty. When a FIFO is used as a destination, Base Address 2182and FW 2188 of the associated S1DSR is read and compared to values fromthe DDSR. If Base Address 2182 of the two DSRs are the same, but FW 2188are different, then the FIFO is full. When a FIFO is used as a source,Base Address 2182 and FW 2188 of the associated DDSR are read andcompared to values from the S1DSR. If Base Address 2182 of the two DSRsare the same and FW 2188 are the same, then the FIFO is empty. In somescenarios (e.g., microthreading), in response to a read accessing anempty FIFO or a write accessing a full FIFO, processing is switched toan instruction in another task until the FIFO is respectively not emptyor not full.

In some embodiments and/or usage scenarios, software (e.g. Task SW onPEs 260 of FIG. 2) configures and operates a FIFO as an extension ofqueues of a PE. For example, a FIFO is enabled to store data elements toprovide capacity in addition to one or more queues of Input Qs 897 andOutput Queues 859. As another example, a FIFO is enabled to provideadditional capacity for the fabric connecting PEs by buffering wavelets.

FIG. 22B illustrates selected details of an embodiment of a 4D MemoryVector Extended Data Structure Descriptor, as 4D Memory Vector ExtendedData Structure Descriptor 2240. In some embodiments, 4D Memory VectorExtended Data Structure Descriptor 2240 partially describes afour-dimensional vector of data elements stored in the memory. 4D MemoryVector Extended Data Structure Descriptor 2240 comprises Type 2241,Dimensions 2242, DF (Dimension Format) 2243, Select Stride 1 2244.1,Select Stride 2 2244.2, Select Stride 3 2244.3, Select Stride 4 2244.4,and Stride 2245. In some embodiments, 4D Memory Vector Extended DataStructure Descriptor 2240 comprises 51 bits.

In some embodiments, Type 2241 comprises a 1-bit field specifying thetype of data structure. Type 2241 is “0” for all instances of 4D MemoryVector Extended Data Structure Descriptor 2240.

In some embodiments, Dimensions 2242 comprises a 20-bit field used toinitialize the length of the next dimension of the vector.

In some embodiments, DF (Dimension Format) 2243 comprises a 5-bit fieldthat, in conjunction with Length 2161 of FIG. 21D, specifies the lengthof each dimension of the N-dimensional vector. Conceptually, Length 2161is divided into six consecutive 4-bit nibbles and each dimension isexpressed using one or more of the nibbles. Bits are asserted in DF 2243to indicate demarcations between the dimensions in Length 2161. Forexample, DF 2242 is “01110” (binary), indicating that the firstdimension is expressed using two nibbles, e.g., bits [7:0], andrepresents a length between 1 and 128. Similarly, the second dimensionis expressed using one nibble, e.g., bits [11:8], and represents alength between 1 and 4. An N-dimension vector is represented byasserting (N−1) bits in DF 2242, and only the last dimension uses morethan four nibbles. In some embodiments and/or usage scenarios, aone-dimensional vector is described using this format, e.g., if thevector is too long for Length 2141 (of FIG. 21C) to describe. In someembodiments and/or usage scenarios, a two-dimensional orthree-dimensional vector is described using this format.

In some embodiments, Select Stride 1 2244.1 comprises a 1-bit fieldspecifying a stride for the first dimension of the vector. If SelectStride 1 2244.1 is “0”, then the stride is 1. If Select Stride 1 2244.1is “1”, then the stride is specified by Stride 2245.

In some embodiments, Select Stride 2 2244.2 comprises a 3-bit field andencodes a stride for the second dimension of the vector. If SelectStride 2 2244.2 is “0”, then the stride is 1. If Select Stride 2 2244.2is “1”, then the stride is specified by Stride 2245. If Stride Select 22244.2 is 2-7, then the stride is specified by a corresponding (DSR)stride register (e.g., of the six stride registers of DSRs 846.

In some embodiments, Select Stride 3 2244.3 and Select Stride 4 2244.4comprise respective 3-bit fields. In some embodiments, Select Stride 32244.3 and Select Stride 4 2244.4 are respectively similar in functionand/or operation with respect to the third and fourth dimension asSelect Stride 2 2244.2 is with respect to the second dimension.

In some embodiments, Stride 2245 comprises a 15-bit field specifying astride of the vector in the memory. In some scenarios, Stride 2245enables using a longer stride for a one-dimensional vector than Stride2153 (of FIG. 21C).

FIG. 23 illustrates selected details of an embodiment of accessingoperands in accordance with data structure descriptors, as DataStructure Descriptor Flow 2300. In some embodiments, actions of DataStructure Descriptor Flow 2300 are performed by a CE (e.g., CE 800).

Accessing a source operand via a data structure descriptor begins (Start2301) by initializing one or more DSRs of a CE of a PE with respectiveDSDs (Set DSR(s) 2302) and optionally initializing respective XDSDsand/or stride values of the CE ((optional) Set XDSR(s) 2305). In someembodiments, the initialized DSRs (as well as the optionally initializedXDSRs and stride registers holding the stride values) are initialized byinstructions that move data from memory to the DSRs. Subsequently, theCE fetches and decodes an instruction (e.g., FMACH, MOV, or LT16)comprising one or more operands specified by the initialized DSRs andoptionally one or more XDSRs and/or stride registers (Fetch/DecodeInstruction with DSR(s) 2303). In some embodiments, the operand typefields of the instruction specify whether an operand is specified by aDSR.

The CE reads one or more DSDs from the DSRs (Read DSR(s) 2304) anddetermines one or more of: the type of data structure, the source of thedata element(s), whether multiple data elements are read together (e.g.,for a SIMD operation), and the total number of data elements for eachoperand. Depending on the determination, for each DSD read, an XDSR andone or more stride registers are also optionally read ((optional) ReadXDSR(s) 2306), as described with respect to FIG. 24. In some scenarios,DSRs are read for one or more of: a source0 operand, a source1 operand,and a destination operand, and are identified by respective operandfields of the instruction obtained in action 2303. In some embodimentsand/or usage scenarios, any one or more of the DSRs, the XDSRs and thestride registers are read entirely or partially in parallel, and inother embodiments and/or usage scenarios, any one or more of the DSRs,the XDSRs and the stride registers are read entirely or partiallysequentially.

Based upon the DSDs obtained in action 2304 (and optional XDSRs andstride values obtained in action 2306), the CE reads one or more sourcedata element(s) from the fabric and/or memory (Read (Next) Source DataElement(s) from Queue/Memory 2310). For each source specified by theinstruction obtained in action 2303 (e.g., each of source0 and source1),the CE reads sufficient elements for an iteration of the operationspecified in the instruction, and in accordance with SIMD widthinformation in the DSDs. In some embodiments and/or usage scenarios,sufficient elements for an iteration is at least one element and no morethan the number indicated by the SIMD width information. In variousembodiments, sufficient elements is no more than the number of elementscomprised by one or two entries in a queue of Input Queues 897 and nomore than the number of elements comprised by one or two entries in aqueue of Output Queues 859. Data element(s) from the fabric (e.g., asource data structure is a fabric vector) are accessed via one or morequeues of the CE. In some embodiments and/or usage scenarios, the CEalso reads data element(s) from registers.

After reading the source data element(s), the CE performs the operationusing the data element(s) as inputs (Perform (Next) Operation(s) on DataElement(s) 2311). The operation is specified by the instruction obtainedin action 2303 (e.g., a multiply-accumulate operation for an FMACHinstruction, a move operation for a MOV instruction, or a less thaninteger comparison for LT16).

In some scenarios, the operation (e.g., a multiply-accumulate operationor a move operation) produces one or more output data element(s). The CEwrites the output data element(s) to the fabric or the memory (Write(Next) Destination Data Element(s) to Queue/Memory 2312), based upon theDSDs obtained in action 2304 (and optional XDSRs and stride valuesobtained in action 2306). Data element(s) sent to the fabric (e.g., thedestination data structure is a fabric vector) are formed into waveletsand transmitted to the fabric via the router of the PE. In some otherscenarios, there are no output data elements (e.g., some comparisonoperations).

After writing any results from the operation, the CE determines if thereare additional data element(s) to process (More Data Element(s)? 2313).In some embodiments, the DSD specifies the total number of data elementsto access (e.g., the length of the vector) and the CE compares thenumber of data element(s) that have been accessed (e.g., tracked via acounter) to the total number of data element(s) specified by the length.If there are additional data element(s) to process, the CE repeatsactions 2310-2313 until all data element(s) have been processed and flowconcludes (End 2316).

In various embodiments and/or usage scenarios, all or any portions ofany one or more of elements of Data Structure Descriptor Flow 2300(e.g., any one or more actions of 2302-2312) correspond conceptually toand/or are related conceptually to operations performed by and/orelements of a CE, e.g., CE 800.

As an example, the source DSRs holding source DSDs (associated with SetDSR(s) 2302 and Read DSR(s) 2304) are one or more of DSRs 846 (e.g.,S0DSRs, S1DSRs, DDSRs, XDSRs, and stride registers). In someembodiments, CE 800 performs Set DSR(s) 2302 responsive toinstruction(s) that write DSDs into DSRs, e.g., LDS0WDS, LDS1WDS, LDXDS,and LDSR.

As another example, CE 800 performs Fetch/Decode Instruction with DSR(s)2303. In various embodiments, PC 834 and I-Seq 836 fetch instructionsfrom Memory 854 and Dec 840 decodes fetched instructions. In someembodiments, instructions are formatted in accordance with one of:Multiple Operand Instruction 2510 of FIG. 25A, One Source, NoDestination Operand Instruction 2520 of FIG. 25B, and ImmediateInstruction 2530 of FIG. 25C. In some embodiments, decoding includesdetecting that an instruction operand is specified by a DSD, e.g., thatthe value of Operand 1 Type 2514.1 is “1”.

As another example, CE 800 performs Read DSR(s) 2304 in response to aninstruction with one or more operands specified by a DSR. In variousembodiments, D-Seq 844 reads the DSR(s) specified by the instructionobtained in action 2303 from DSRs 846. In some embodiments, DSDs readfrom the DSRs are formatted in accordance with one or more of: FabricInput Data Structure Descriptor 2100 of FIG. 21A, Fabric Output DataStructure Descriptor 2200 of FIG. 21B, 1D Memory Vector Data StructureDescriptor 2140 of FIG. 21C, 4D Memory Vector Data Structure Descriptor2160 of FIG. 21D, and Circular Memory Buffer Data Structure Descriptor2180 of FIG. 21E. In some embodiments and/or usage scenarios, D-Seq 844,e.g., responsive to DSDs having Type 2169 or Type 2189 specifying anXDSR, performs (optional) Read XDSR(s) 2306. In various embodiments,XDSDs read from the XDSRs are formatted in accordance with one of:Circular Memory Extended Buffer Data Structure Descriptor 2180 of FIG.22A and 4D Memory Vector Extended Data Structure Descriptor 2160 of FIG.22B.

As another example, CE 800 performs Read (Next) Source Data Element(s)from Queue/Memory 2310 based upon the source DSD(s) read in action 2304and optionally XDSD(s) read in action 2306. In some scenarios, a sourceDSD specifies (e.g., via Type 2149) that an operand originates frommemory, and D-Seq 844 reads data element(s) from D-Store 848 or Memory854 at address(es) specified by the DSD (e.g., based in part upon one ormore of: Base Address 2142, WLI 2152, and Stride 2153). In somescenarios, a source DSD specifies (e.g., via Type 2109) that an operandoriginates from the fabric and CE 800 reads data element(s) from one ofInput Qs 897. In some embodiments and/or usage scenarios, data elementsare directly transmitted from one of Input Qs 897 to Data Path 852. Inother embodiments and/or usage scenarios, data elements are transmittedfrom one of Input Qs 897 to RF 842 and from RF to Data Path 852. In someembodiments, the one of Input Qs 897 is implicitly specified by portionsof the DSD (e.g., one or more of: UTID 2102, SC 2112, and SQ 2113). Insome scenarios, the CE reads from the queue associated with the color ofthe current task (e.g., the task associated with the instructionobtained in action 2303). In some scenarios (e.g., SQ 2113 is “1”), theCE reads from a queue specified by UTID 2102. In some scenarios (e.g.,SC 2112 is “1”), the CE reads from a queue associated with the colorspecified by UTID 2102 concatenated with CH 2114. In some scenarios, theCE reads one, two, or four data elements from the specified queue basedupon SW 2104.

In some embodiments and/or usage scenarios, when CE 800 attempts to readmore data element(s) than are available in the specified queue of InputQs 897, or alternatively attempts to read from an empty FIFO (e.g., asimplemented in accordance with a DSD in accordance with FIG. 21E), thenCE 800 stalls. In some embodiments and/or usage scenarios (e.g.,microthreading), Picker 830 is enabled to select a different task fromInput Qs 897 while waiting for the data element(s), thereby enabling CE800 to avoid stalling. Microthreading is described in more detail inFIG. 26 and section “Microthreading”.

As another example, CE 800 performs Perform (Next) Operation(s) on DataElement(s) 2311. In some embodiments, Data Path 852 uses the dataelement(s) read in action 2310 as inputs to the operation specified bythe instruction obtained in action 2303. In some scenarios (e.g., acomputational operation), action 2311 produces output data element(s),while in other scenarios (e.g., a comparison operation), action 2311produces no output data element. In some embodiments, Data Path 852 isenabled to perform more than one operation simultaneously (e.g., in aniteration), e.g., performing two or four multiply-accumulate operationssimultaneously using SIMD execution resources.

As another example, CE 800 performs Write (Next) Source Data Element(s)to Queue/Memory 2312 based upon the destination DSD read in action 2304and optionally XDSD(s) read in action 2306. In some scenarios, thedestination DSD specifies (e.g., via Type 2149) that an operand isdestined for memory, and D-Seq 844 writes data element(s) to D-Store 848or Memory 854 at address(es) specified by the destination DSD (e.g.,based in part upon one or more of: Base Address 2142, WLI 2152, andStride 2153).

In various embodiments and/or usage scenarios, portions of action 2312(e.g., writing destination data elements to the fabric) correspondconceptually to and/or are related conceptually to Provide DataElement(s) as Wavelet to Output Queue 1408 of FIG. 14. In somescenarios, a destination DSD specifies (e.g., via Type 2129) that anoperand is sent to the fabric and CE 800 creates wavelet(s) (e.g., basedin part upon Fabric Output Data Structure Descriptor 2120) from the dataelement(s) and transmits them via Output Queues 859 and On Ramp 860 toRouter 600 (of FIG. 6) to the fabric. In some scenarios, the CEtransmits one, two, or four data elements as wavelets, based upon SW2124 of the destination DSD.

In some embodiments and/or usage scenarios, when CE 800 attempts totransmit more wavelets than resources available in Router 600 (e.g.,there are insufficient resources in Data Queues 650 of FIG. 6), oralternatively attempts to write to a full FIFO (e.g., as implemented inaccordance with a DSD in accordance with FIG. 21E), then CE 800 stalls.In some embodiments and/or usage scenarios (e.g., microthreading),Picker 830 is enabled to select a different task from Input Qs 897 whilewaiting for more resources, thereby enabling CE 800 to avoid stalling.Microthreading is described in more detail in FIG. 26 and section“Microthreading”.

As another example, CE 800 performs action 2313. In some embodiments,D-Seq 844 determines how many data element(s) have been processed (e.g.,by incrementing a counter for each data element) and compares thisagainst the length of the vector (e.g., Length 2101).

FIG. 24 illustrates selected details of an embodiment of decoding a datastructure descriptor, as Data Structure Descriptor Decode Flow 2400. Invarious embodiments and/or usage scenarios, Memory Data StructureDescriptor Flow 2400 is a conceptual representation of all or anyportions of actions 2304, 2306, 2310, and 2312 (of FIG. 23) as performedfor each DSR describing a fabric or a memory vector. In summary, FIG. 23illustrates fetching and decoding an instruction comprising one or moreoperands specified by initialized DSRs, reading the DSRs to obtain anddecode corresponding DSDs, reading (next) source data elements inaccordance with the DSDs, performing an operation on the source dataelements, writing output data elements of the operation in accordancewith the DSDs, and iterating back to reading the next source dataelements until complete. FIG. 24 illustrates, for fabric vectors (FabricVector 2410) and memory vectors (Memory Vector 2420), further detailsregarding decoding the DSDs obtained from the DSRs, as well asoptionally reading one or more XDSRs and stride registers to obtain anddecode corresponding XDSDs and stride values, to determine memory accesspatterns used to access data elements of the memory vectors of theinstruction (e.g., any one or more of source0, source1, anddestination). Conceptually, the actions illustrated in FIG. 24 areperformed for each DSD obtained via action 2304 of FIG. 23. In someembodiments, actions of Memory Data Structure Descriptor Flow 2400 areperformed by a CE (e.g., CE 800).

Decoding a DSD (e.g., as obtained via action 2304 of FIG. 23) begins(Start 2401) by the CE determining whether the DSD corresponds to afabric vector (Type=Fabric? 2411), e.g., in accordance with FIG. 21A orFIG. 21B. If so, then accesses of the operand described by the DSDproceed as a fabric vector using the DSD (Access via DSD 2412), e.g., ifthe operand is a source (FIG. 21A), then action 2310 (of FIG. 23) readsfrom the fabric in accordance with the DSD, and if the operand is adestination (FIG. 21B), then action 2312 (of FIG. 23) writes to thefabric in accordance with the DSD. Decoding the DSD is then complete(End 2499).

If the DSD does not correspond to a fabric vector, then the DSDcorresponds to a memory vector. The CE then determines whether the DSDcorresponds to a 1D memory vector (Type=XDSR? 2421), e.g., in accordancewith FIG. 21C. If so, then accesses of the operand described by the DSDproceed as a 1D memory vector using the DSD (Access 1D via DSD 2427).E.g., if the operand is a source, then action 2310 reads the source fromthe memory in accordance with a 1D memory vector described by the DSD,and if the operand is a destination, then action 2312 writes to thememory in accordance with a 1D memory vector described by the DSD.Decoding the DSD is then complete (End 2499). Each iteration of dataelements in FIG. 23 (actions 2310-2313) advances the operand memoryaddresses in accordance with the 1D memory vector described by the DSD.

If the DSD does not correspond to a 1D memory vector, then the DSDcorresponds to either a 4D memory vector (e.g., in accordance with FIG.21D) or a circular buffer (e.g., in accordance with FIG. 21E). The CEreads an XDSR specified by the DSD (Read XDSR Specified via DSD 2422,also conceptually corresponding to (optional) Read XDSR(s) 2306 of FIG.23) to obtain an XDSD. The XDSR is specified by Type 2169 (of FIG. 21D)or Type 2189 (of FIG. 21E).

The CE then determines whether the XDSD specifies a 4D memory vector(e.g., in accordance with FIG. 22B). If so, then the CE optionally readsone or more stride registers ((optionally) Read Stride Register(s) 2424,also conceptually corresponding to (optional) Read XDSR(s) 2306 of FIG.23), as optionally specified by the XDSD. Accesses of the operanddescribed by the DSD, the XDSD, and any optional stride values (obtainedfrom the stride registers) proceed as a 4D memory vector using the DSD,the XDSD, and the optional stride values (Access 4D via XDSD 2428).E.g., if the operand is a source, then action 2310 reads the source fromthe memory in accordance with the 4D memory vector, and if the operandis a destination, then action 2312 writes to the memory in accordancewith the 4D memory vector. Decoding the DSD is then complete (End 2499).Each iteration of data elements in FIG. 23 (actions 2310-2313) advancesthe operand memory addresses in accordance with the 4D memory vectordescribed by the DSD.

If the XDSD does not correspond to a 4D memory vector, then the XDSDcorresponds to a circular buffer (e.g., in accordance with FIG. 22A).Accesses of the operand described by the DSD and the XDSD proceed as acircular buffer using the DSD and the XDSD (Access Circular Buffer viaXDSD 2429). E.g., if the operand is a source, then action 2310 reads thesource from the memory in accordance with the circular buffer, and ifthe operand is a destination, then action 2312 writes to the memory inaccordance with the circular buffer. Decoding the DSD is then complete(End 2499). Each iteration of data elements in FIG. 23 (actions2310-2313) advances the operand memory addresses in accordance with thecircular buffer described by the DSD.

In various embodiments, D-Seq 844 performs Type=Fabric? 2411 and/orType=XDSD? 2421 based upon a DSD read in action 2304 (of FIG. 23). Insome embodiments, a type field of the DSD (e.g., Type 2109 of FIG. 21A,Type 2129 of FIG. 21B, Type 2149 of FIG. 21C, Type 2169 of FIG. 21D, orType 2189 of FIG. 21E) determines if the data structure is one of: afabric vector (e.g., the Type=“0”), a 1D vector (e.g., the Type=“1”),and an XDSD type (e.g., the Type=“2-7”). In various embodiments (e.g.,the Type=“2-7”), the value of the type field specifies which XDSR ofDSRs 846 to read for action 2422. In some embodiments, D-Seq 844performs action 2422 and receives the XDSD from DSRs 846. In some otherembodiments, DSRs 846 performs actions 2421 and 2422 and transmits theDSD and the XDSD to D-Seq 844.

As another example, D-Seq 844 performs Type=4D Vector? 2423 based uponthe XDSD of action 2422. In some embodiments, the type field of the XDSD(e.g., Type 2211 of FIG. 22A or Type 2241 of FIG. 22B) read from theXDSR determines if the data structure is one of a 4D vector (e.g., theXDSD Type=“0”) and a circular buffer (the XDSD Type=“1”).

As another example, D-Seq 844 generates memory access(es) in accordancewith action 2427 by computing the memory address(es) based upon the DSD(e.g., of action 2304), using e.g., Base Address 2142, WLI 2152, Length2141, and Stride 2153 of the DSD, as described elsewhere herein.Similarly, D-Seq 844 generates memory access(es) in accordance withaction 2428 by computing the memory address(es) based upon the DSD(e.g., of action 2404) and XDSD of action 2422 using e.g., Base Address2162, Length 2161, WLI 2172, Stride 2245, Stride Select 1 2244.1, and DF2243 of the DSD and the XDSD, as described elsewhere herein. Similarly,D-Seq 844 generates memory access(es) in accordance with action 2429 bycomputing the memory address(es) based upon the DSD (e.g., of action2404) and XDSD of action 2422 using e.g., Base Address 2182, Length2181, WLI 2192, Start Address 2212, and End Address 2213 of the DSD andthe XDSD, as described elsewhere herein.

In some embodiments, D-Seq 844 sends each computed address to one ofD-Store 848 and Memory 854. In response to receiving a computed address,the D-Store and/or the Memory accesses two bytes of data at the computedaddress.

Instruction Formats

Each element identifier in the description of FIGS. 25A-C having a firstdigit of “8” refers to an element of FIG. 8, and for brevity is nototherwise specifically identified as being an element of FIG. 8.

FIG. 25A illustrates selected details of an embodiment of a multipleoperand instruction, as Multiple Operand Instruction 2510. MultipleOperand Instruction 2510 is one of: a two/three source, one destinationoperand instruction (e.g., a multiply-add such as FMACH), a two source,no destination operand instruction (e.g., a comparison such as LT16),and a one source, one destination operand instruction (e.g., a moveinstruction such as MOV16).

Multiple Operand Instruction 2510 comprises various fields: InstructionType 2511, Opcode 2512, Operand 0 Encoding 2513, Operand 1 Encoding2514, and Terminate 2515. Operand 0 Encoding 2513 comprises Operand 0Type 2513.1 and Operand 0 2513.2. Operand 1 Encoding 2514 comprisesOperand 1 Type 2514.1 and Operand 1 2514.2. In some embodiments,Multiple Operand Instruction 2510 comprises 20 bits.

In some embodiments, the value of Instruction Type 2511 distinguishesbetween different types of instructions (e.g., two/three source, onedestination and one source, and one destination instruction types)according to the table following. In various embodiments, the value ofOpcode 2512 specifies a particular operation (e.g., multiply, add, orsubtract). The length of Opcode 2512 varies between different types ofinstructions as described in the table following.

Value of Length of Instruction Opcode Instruction Family Type 2511 2522Two/three source, one destination 10 5 bits Two source, no destination1110 4 bits One source, one destination 110 5 bits

In some embodiments, Operand 0 Encoding 2513 describes a source and/ordestination operand, according to the table following. In someembodiments, Operand 1 Encoding 2714 describes a source operand.

Operand 1 Operand 0 Encoding Instruction Family Encoding 2513 2514Two/three source, one destination Source0 and destination Source1 Twosource, no destination Source0 Source1 One source, one destinationDestination Source1

In some embodiments, Operand 0 2513.2 and Operand 1 2514.2 compriserespective 4-bit fields. In some embodiments, Operand 0 Type 2513.1 andOperand 1 Type 2514.1 comprise respective 2-bit fields and respectivelydetermine how to interpret Operand 0 2513.2 and Operand 1 2514.2. For atwo/three source operand, one destination operand instruction, Operand 0Type 2513.1 is interpreted according to the table following.

Value of 2513.1 Operand 0 Encoding 2513 0 Source0 is S0DSR[Operand 02513.2], destination is S0DSR[Operand 0 2513.1] 1 Source0 isS0DSR[Operand 0 2513.2], destination is DDSR[Operand 0 2513.1] 2 Source0is GPR[Operand 0 2513.2], destination is GPR[Operand 0 2513.1] 3 Source0is GPR[Operand 0 2513.2], destination is DDSR[Operand 0 2513.1] ifOperand 1 Type 2514.1 is 0, destination is GPR[0] otherwise

For example, if the value of Operand 0 Type 2513.1 is “1” and the valueof Operand 0 2513.2 is “4”, then Operand 0 Encoding 2513 specifies thatthe source0 operand is a vector described by SODSR[4] and thedestination operand is a vector described by DDSR[4].

For a two source operand, no destination operand instruction, Operand 0Type 2513.1 is interpreted according to the table following.

Value of 2513.1 Operand 0 Encoding 2513 0 Source0 is S0DSR[Operand 02513.2] 1 Source0 is GPR[Operand 0 2513.2]

For example, if the value of Operand 0 Type 2513.1 is “0” and the valueof Operand 0 2513.2 is “4”, then Operand 0 Encoding 2513 specifies thatthe source0 operand is a vector described by SODSR[4].

For a one source operand, one destination operand instruction, Operand 0Type 2513.1 is interpreted according to the table following.

Value of 2513.1 Operand 0 Encoding 2513 0 Destination is DDSR[Operand 02513.2] 1 Destination is GPR[Operand 0 2513.2]

For example, if the value of Operand 0 Type 2513.1 is “0” and the valueof Operand 0 2513.2 is “4”, then Operand 0 Encoding 2513 specifies thatthe destination operand is a vector described by DDSR[4].

For Multiple Operand Instruction 2510, Operand 1 Type 2514.1 isinterpreted according to the table following.

Value of 2514.1 Operand 1 Encoding 2514 0 Source1 is S1DSR[Operand 12514.2] 1 Source1 is the data in memory at the address specified byGPR[6] 2 Source1 is GPR[Operand 1 2514.2] 3 Source1 is an immediate

For example, if the value of Operand 0 Type 2513.1 is “0” and the valueof Operand 0 2513.2 is “4”, then Operand 0 Encoding 2513 specifies thatthe destination operand is a vector described by DDSR[4].

In various embodiments, a source1 operand that is an immediate specifiesone of: several predetermined values (e.g., 0, 1, and −1) and apseudo-random number generated by an LFSR. For example, if the value ofOperand 1 Type 2514.1 is “3” and the value of Operand 1 2514.2 is “8”,then Operand 1 Encoding 2514 specifies a PRN generated by an LFSR.

In various embodiments, a source1 operand that is a floating-pointimmediate specifies one of: several predetermined values (e.g., 0, 1,−1, +infinity, −infinity, min normal, max normal, −min normal, −minnormal) and a pseudo-random number generated by an LFSR. For example, ifthe value of Operand 1 Type 2514.1 is “3” and the value of Operand 12514.2 is “8”, then Operand 1 Encoding 2514 specifies a PRN generated byan LFSR.

In some embodiments, Terminate 2515 comprises a 1-bit field specifyingthat the instruction is the last instruction in a task. When theinstruction finishes execution, the task is terminated, enablingselection and execution of a new task (e.g., via Terminate 812 andPicker 830).

FIG. 25B illustrates selected details of an embodiment of a one source,no destination operand instruction, as One Source, No DestinationInstruction 2520. One Source, No Destination Instruction 2520 comprisesInstruction Type 2521, Opcode 2522, Operand 1 Encoding 2523, ImmediateHigh 2524, and Terminate 2525. Operand 1 Encoding 2523 describes asource operand and comprises Operand 1 Type 2523.1 and Operand 1 2523.2.In some embodiments, One Source, No Destination Instruction 2520comprises 20 bits.

In some embodiments, Instruction Type 2521 comprises four bits, “1111”,specifying that the instruction is a one source, no destination operandinstruction, and Opcode 2522 comprises a 4-bit field specifying aparticular operation (e.g., block, unblock, activate, set active PRNG,data filter, conditional branch, and jump).

In some embodiments, Immediate High 2524 comprises a 4-bit field. Insome scenarios, Immediate High 2524 concatenated with Operand 1 2523.2forms an 8-bit immediate.

In some embodiments, Operand 1 Type 2523.1 comprises a 2-bit field thatdetermines how Operand 1 2523.2 is interpreted. If Operand 1 Type 2523.1is “0”, then Operand 1 Encoding 2523 specifies a vector (e.g., a fabricvector of data elements from Input Qs 897, or a memory vector of dataelements in one of Memory 854 and D-Store 854) and the value of Operand1 2523.2 identifies which one of the 12 S1DSRs of DSRs 846 describe thevector. If Operand 1 Type 2523.1 is “1”, then Operand 1 Encoding 2523describes a value in memory (e.g., one of Memory 854 and D-Store 848) atan 8-bit address formed by a concatenation of Immediate High 2524 withOperand 1 2523.2. If Operand 1 Type 2523.1 is “2”, then Operand 1Encoding 2523 describes a value in a register (e.g., one of RF 842)identified by the value of Operand 1 2523.2. If Operand 1 Type 2523.1 is“3”, then Operand 1 Encoding 2523 describes an immediate. If Opcode 2522specifies an operation (e.g., block, unblock, or activate) that operateson 16-bit integer operands, then the immediate comprises eight bits andis a concatenation of Immediate High 2524 and Operand 1 2523.2.

In some embodiments, Terminate 2525 comprises a 1-bit field specifyingthat the instruction is the last instruction in a task. When theinstruction finishes execution, the task is terminated, enablingselection and execution of a new task (e.g., via Terminate 812 andPicker 830. If One Source, No Destination Instruction 2520 is aconditional branch, then the task is only terminated if the conditionalbranch is not taken.

FIG. 25C illustrates selected details of an embodiment of an immediateinstruction, as Immediate Instruction 2530. Immediate Instruction 2530comprises Instruction Type 2531, Opcode 2532, Operand 0 2533.2, andImmediate 2534. In some embodiments, Immediate Low 2534.1 comprises a9-bit field and Immediate High 2534.2 comprises a 1-bit field. Theconcatenation of Immediate Low 2534.1 and Immediate High 2534.2 iscollectively referred to (and illustrated as) as Immediate 2534. In someembodiments, Immediate Instruction 2520 comprises 20 bits.

In some embodiments, Instruction Type 2531 comprises a 1-bit field, “0”,specifying that the instruction is an immediate instruction, and Opcode2532 comprises a 5-bit field specifying a particular operation (e.g.,load source0 DSR, load source1 DSR, load destination DSR, store source0DSR, store source1 DSR, and store destination DSR). In some scenarios,execution of an Immediate Instruction 2530 (e.g., a load DSRinstruction, and a load XDSR instruction) loads data from one of Memory854 and D-Store 848 to a DSR of DSRs 846. In other scenarios, executionof an Immediate Instruction 2530 (e.g., a store DSR instruction, and astore XDSR instruction) stores data from a DSR of DSRs 846 to one ofMemory 854 and D-Store 848.

In some embodiments, Operand 0 2533.2 comprises a 4-bit field and Opcode2532 determines how Operand 0 2533.2 is interpreted. In some scenarios(e.g., if Operand 0 2533.2 specifies an operation without a registeroperand such as a jump operation), Immediate Low 2534.1, Operand 02533.2, and Immediate High 2534.2 are concatenated to form a 14-bitimmediate. In some other scenarios, Immediate 2534 is sign extended toform a 16-bit immediate. In yet other scenarios, Immediate 2534 is signextended to form a 15-bit address. In yet other scenarios, Immediate2534 is shifted one bit to the left and sign extended to form a 15-bitaddress (e.g., for 32-bit data).

Microthreading

FIG. 26 illustrates selected details of processing in accordance with amicrothreaded instruction, as Microthreading Instruction Flow 2600. Insome embodiments, actions of flow 2600 are performed by a CE (e.g., CE800). In various embodiments and/or usage scenarios, flow 2600 isconceptually related to flow 2300 of FIG. 23, Fabric Input DataStructure Descriptor 2100 of FIG. 21A, and Fabric Output Data StructureDescriptor 2120 of FIG. 21B.

Flow 2600 is descriptive of processing that occurs in the context ofData Structure Descriptor Flow 2300 of FIG. 23. Specifically, flow 2600illustrates, as Read (Next) Source Data Element(s) from Queue/Memory2310A, an alternate embodiment of Read (Next) Source Data Element(s)from Queue/Memory 2310 of FIG. 23, illustrating various details ofprocessing relating to microthreading. As in the context of FIG. 23,processing begins by the CE reading one or more DSDs from the DSRs (ReadDSR(s) 2304). In some scenarios, DSRs are read for one or more of: asource0 operand, a source1 operand, and a destination operand. Basedupon the DSD(s) and the status of one or more of fabric inputs, fabricoutputs, FIFO inputs, and FIFO outputs, the CE determines if a stallcondition exists (Stall? 2603). When no stall condition exists, the CEreads one or more source data element(s) from the fabric and/or memory(Read (Next) Source Data Element(s) from Queue/Memory 2610).

When a stall condition exists, the CE determines if microthreading isenabled (Microthreading Enabled? 2606) for the instruction fetched inFetch/Decode Instruction with DSR(s) 2303 of FIG. 23. If so, then the CEsaves information about the microthreaded instruction (e.g., updatedlength of DSD(s), the cause of the stall, and/or all or any portions ofthe instruction itself) (Save Microthreaded Instruction Information2607). The CE executes the next instructions (Execute NextInstruction(s) 2608). In some embodiments and/or usage scenarios, thenext instruction is the instruction immediately following themicrothreaded instruction. In some other embodiments and/or usagemodels, the next instruction is part of a different task (e.g., a taskselected by the scheduler for execution).

The CE periodically, e.g., every core clock cycle, monitors the stallcondition(s) (e.g., detected at action 2603) to detect if the stallcondition(s) have abated and the operands are ready (Stall Resolved?2609). When the stall has not resolved, the CE continues executing thenext instructions (action 2608). When the stall has been resolved, theCE resumes executing the microthreaded instruction by reading sourcedata elements (Read (Next) Source Data Element(s) from Queue/Memory2610), thereby concluding flow. If microthreading is not enabled, thenthe CE stalls processing until the stall condition(s) have abated andthe operands are ready (Stall Resolved? 2605). When the stall has beenresolved, the CE resumes executing the instruction by reading sourcedata elements (Read (Next) Source Data Element(s) from Queue/Memory2610), thereby concluding flow.

In various embodiments and/or usage scenarios, actions of flow 2600 areconceptually related to a CE, e.g., CE 800 of FIG. 8. Action 2304 is aspecific example of Action 2304 of FIG. 23, wherein at least one of theDSRs holds a fabric DSD (e.g., in accordance with one of Fabric InputData Structure Descriptor 2100 of FIG. 21A and Fabric Output DataStructure Descriptor 2120 of FIG. 21B) that enables microthreading(e.g., one of UE 2103 and UE 2123 is respectively enabled). In someembodiments, a stall is caused by one or more of: a destination FIFO(e.g., in accordance with Circular Memory Buffer Data StructureDescriptor 2180 of FIG. 21E and Circular Memory Buffer Extended DataStructure Descriptor 2210 of FIG. 22A) that has insufficient space fordata element(s), a source FIFO that has insufficient data element(s), asource fabric vector on a virtual channel with an input queue withinsufficient data element(s) (e.g., one of Input Qs 897), and adestination fabric vector on a virtual channel with an output queue thathas insufficient space for data element(s) (e.g., one of Output Queues859). In some embodiments and/or usage scenarios, the sufficient numberof data elements and/or the sufficient space is determined in accordancewith the SIMD width of the DSD(s) read in Action 2304 (e.g., SW 2104 ofFabric Input Data Structure Descriptor 2100 of FIG. 21A).

In some embodiments and/or usage scenarios, action 2607 savesinformation about the microthreaded instruction (e.g., from Dec 840) toUT State 845. In various embodiments, the information comprises one ormore of: stall condition(s) to monitor in action 2609 (e.g., waiting forone or more of: a FIFO with insufficient space, a FIFO with insufficientdata element(s), a fabric input, and a fabric output), portions of theDSD(s) (e.g., information identifying a queue from one or more of D-Seq844 and DSRs 846), and/or all or any portions of the instruction itself.In various embodiments, the CE writes associated state to the respectiveDSD(s) that were read in action 2304. For example, a microthreadedinstruction that specifies reading 32 data elements from fabric inputand writing the 32 data elements to a 1D memory vector is stalled afterreading and writing four data elements. Length 2101 of the source DSDand Length 2141 of the destination DSD are written indicating that thelength is now 28 data elements. The CE also writes the next address toBase Address 2142 of the destination DSD (e.g., increment the address bythe length of four data elements times Stride 2153). In some otherembodiments, the CE writes the all or any portions of the instructioninformation to a shadow version(s) of the respective DSD(s) read inaction 2304.

In some embodiments and/or usage scenarios, action 2610 is performed inaccordance with the information stored about the microthreadedinstruction in UT State 845 and the respective DSD(s) that were updatedin action 2607. For example, when action 2609 flows to action 2610, apartial restore is optionally and/or selectively performed by readinginformation from UT State 845. In various other embodiments, action 2610is performed in accordance with the information stored about themicrothreaded instruction in UT State 845 and the respective shadowversion(s) of the DSD(s) that were updated in action 2607. For example,when action 2609 flows to action 2610, a partial restore is optionallyand/or selectively performed by reading information from any combinationof UT State 845 and the respective shadow version(s) of the DSD(s) thatwere updated in action 2607.

Deep Learning Accelerator Example Uses

In various embodiments and/or usage scenarios, as described elsewhereherein, a deep learning accelerator, such as a fabric of PEs (e.g., asimplemented via wafer-scale integration and as illustrated, for example,in FIG. 4) is usable to train a neural network, and/or to performinferences with respect to a trained neural network. The training, insome circumstances, comprises determining weights of the neural networkin response to training stimuli. Various techniques are usable for thetraining, such as Stochastic Gradient Descent (SGD), Mini-Batch GradientDescent (MBGD), Continuous Propagation Gradient Descent (CPGD), andReverse CheckPoint (RCP). Following, CPGD is contrasted with othertechniques, and then each of SGD, MBGD, CPGD, and RCP are described inmore detail.

Past deep neural network training approaches (e.g., SGD and MBGD) haveused so-called anchored-delta learning. That is, the delta derivedweight updates have been ‘anchored’ or held fixed until processing ofall activations for a training set batch or a mini-batch are completed.In some circumstances, the layer-sequential nature of anchored-deltalearning resulted in high-latency sequential parameter updates(including for example, weight updates), which in turn led to slowconvergence. In some circumstances, anchored-delta learning has limitedlayer-parallelism and thus limited concurrency.

In contrast, in some circumstances, use of a continuous propagation (akaimmediate-delta) learning rule for deep neural network training, astaught herein, provides faster convergence, decreases the latency ofparameter updates, and increases concurrency by enablinglayer-parallelism. Deltas computed from the immediate network parametersuse updated information corresponding to the current parameter slope.Continuous propagation enables layer parallelism by enabling each layerto learn concurrently with others without explicit synchronization. As aresult, parallelization along the depth of a network enables morecomputing resources to be applied to training. Parallelism available incontinuous propagation realizes up to a 10× wall clock time improvement,as compared to MBGD techniques, in some usage scenarios. The continuouspropagation approach also enables avoiding using extra memory to storethe model parameter values for multiple vectors of activations.

In some embodiments and/or usage scenarios, a neural network is trainedusing continuous propagation of stimuli to perform SGD. In someembodiments of training via CPGD, RCP enables reducing the number ofactivations held in memory (thus reducing the memory footprint) byrecomputing selected activations. In some scenarios, recomputingactivations also improves the accuracy of the training estimates for theweights. In training without RCP, every layer of neurons receivesactivations during one or more forward passes, and saves the activationsto re-use for computations performed during the one or more backwardpasses associated with the forward passes (e.g., the one or more delta,chain, and weight update passes associated with the forward passes). Insome scenarios (e.g., relatively deep neural networks), the time betweensaving the activations and the associated backward pass is relativelylong and saving all activations uses relatively more memory than savingfewer than all the activations.

For example, only some of the layers of neurons (e.g., every even layer)save the respective activations and the other layers discard therespective activations (e.g., every odd layer). The layers with savedactivations (e.g., every even layer) use the most recent weights torecompute and transmit the recomputed activations to the layers thatdiscarded activations (e.g., every odd layer). In some scenarios, therecomputed activations differ from the discarded activations because themost recent weights are different from the weights that were availableduring the forward pass (e.g., one or more weight updates occurredbetween the forward pass and the associated backward pass). In variousembodiments, the number and type of layers that save and discardactivations is selected to optimize for the desired balance of reducedmemory usage and increased computation. As one example, every fourthlayer saves activations and all other layers discard activations. Asanother example, convolutional layers are selected to save activationsand other layers are selected to discard activations.

In various embodiments and/or usage scenarios, any one or more of SGD,MBGD, and CPGD, with or without RCP, are implemented via one or more of:a fabric of processing elements (e.g., as illustrated in FIG. 4), one ormore GPUs, one or more CPUs, one or more DSPs, one or more FPGAs, andone or more ASICs.

SGD, e.g., with back-propagation, is usable (as described elsewhereherein) for training a neural network. However, learning via gradientdescent is inherently sequential, because each weight update usesinformation from a gradient measurement made after completion of a fullforward pass through the neural network. Further, weight updates aremade during a corresponding backward pass through the neural network(following and corresponding to the forward pass), and thus the lastweight update occurs after completion of the entire correspondingbackward pass.

MBGD enables more parallelism than SGD by gradient averaging over amini-batch, processing several (a ‘mini-batch’ of) activations inparallel. However, speed of sequential updates, compared to SGD, isunchanged, and weight updates, as in SGD, are completed after completionof all corresponding backward passes through the neural network. Asmini-batch size increases by processing more activations in parallel,gradient noise is reduced. Beyond a point the reduction in gradientnoise, in some scenarios, results in poor generalization.

CPGD enables parallel processing and updating of weights in all layersof a neural network, while activations propagate through the layers in astream. Thus, CPGD overcomes, in some embodiments and/or usagescenarios, sequential processing limitations of SGD and MBGD.

RCP enables reduced memory usage via (re)computing activations thatwould otherwise be stored, and is usable in combination with SGD, MBGD,and CPGD.

Pipeline flow diagrams are usable to compare and contrast various SGD,MBGD, CPGD, and CPGD with RCP techniques. Information flows andconcurrency in training techniques are visible with the pipeline flowdiagrams. FIGS. 27A-D illustrate embodiments of pipeline flows forlayers of a neural network flow from left to right, e.g., activationsenter from the left and forward pass propagation of layer computationsflows to the right. A gradient computation is performed in the rightmostlayer to begin the backward pass propagation of layer computationsincluding weight updates from right to left. Time advances from top tobottom.

FIG. 27A illustrates an embodiment of a pipeline flow for SGD. Weightupdates of layers of a neural network are completed after completion ofa corresponding full forward pass and a corresponding full backward passthrough all the layers of the neural network. A next forward pass beginsonly after completion of weight updates corresponding with animmediately preceding forward pass. As illustrated, First Forward Pass2711 is performed (from the first layer to the last layer, illustratedleft to right in the figure). Then First Backward Pass 2721 is performed(from the last layer to the first layer, illustrated right to left inthe figure). During First Backward Pass 2721, weights are updated, fromthe last layer to the first layer. The last weight update (of the firstlayer) is completed as First Backward Pass 7621 completes. Then SecondForward Pass 2712 is performed (using the weights updated during FirstBackward Pass 2721), followed by Second Backward Pass 2722, during whichweight updates are performed.

FIG. 27B illustrates an embodiment of a pipeline flow for MBGD. Aplurality of activations is processed with identical weights.Coordinated quiet times are used to synchronize weight updates. In someembodiments and/or usage scenarios, MBGD processing is characterized byMini-Batch Size (N) 2731, Overhead 2732, and Update Interval (U) 2733.

Unlike gradient-descent techniques (e.g., SGD and MBGD) that use a fullforward pass and a full backward pass through a network to compute agradient estimate, and thus result in a sequential dependency, CPGD usesa differential construction to replace the sequential dependency with acontinuous model that has sustained gradient generation. In someembodiments and/or usage scenarios, CPGD enables layer parallelism byenabling each layer of a neural network to be trained (e.g., to ‘learn’)concurrently with others of the layers without explicit synchronization.Thus, parallelization along the depth of a neural network enablesapplying more computing resources to training. In various embodimentsand/or usage scenarios, CPGD provides comparable accuracy and improvedconvergence rate expressed in epochs of training compared to othertechniques.

FIG. 27C illustrates an embodiment of a pipeline flow for CPGD. CPGDprocessing maintains a model in flux. Hidden representations and deltasenter every layer at every time step, and weights update at every timestep. The CPGD processing is a coordinated synchronous operation. Insome embodiments and/or usage scenarios, CPGD processing ischaracterized by Forward Pass 2751 and a corresponding Backward Pass2761, respectively representing one of a number of forward passes andone of a number of corresponding backward passes. In operation,respective forward passes of a plurality of forward passes operate inparallel with each other, respective backward passes of a plurality ofbackward passes operate in parallel with each other, and the pluralitiesof forward passes and the pluralities of backward passes operate inparallel with each other. Weight updates (made during backward passes)are used by forward passes and backward passes as soon as the weightupdates are available.

As a specific example, Forward Pass 2765 begins, and later Forward Pass2766 begins. At least a portion of Forward Pass 2765 operates inparallel with at least a portion of Forward Pass 2766. At least aportion of a corresponding backward pass for Forward Pass 2765 operatesin parallel with at least a portion of Forward Pass 2766. Further, thecorresponding backward pass completes at least some weight updates thatare used by Forward Pass 2766, as shown by example Weight Update Use2767.

FIG. 27D illustrates an embodiment of a pipeline flow for CPGD with RCP.CPGD with RCP omits saving selected activations, instead recomputing theselected activations. In some embodiments and/or usage scenarios, therecomputing is performed with updated weights. Thus, reverse checkpointenables reduced memory (illustrated as reduced area covered by verticallines passing saved hidden representations forward in time) and reducestime disparity between calculated hidden representations andcorresponding deltas.

As a specific example, CPGD with RCP processing is characterized byForward Pass 2771 and a corresponding Backward Pass 2781. A firstactivation is computed during the Forward Pass and stored in a layer foruse in the corresponding Backward Pass, as illustrated by ActivationStorage 2785. Activation Storage 2785 is occupied during portions ofForward Pass and Backward Pass and unavailable for other uses. Aspecific example of memory reduction is illustrated by RecomputedActivation Storage 2786. A second activation is computed during theForward Pass, but is discarded and does not require any storage. Duringthe Backward Pass the second activation is recomputed and stored in alayer for use in the Backward Pass as illustrated by RecomputedActivation Storage 2786. Recomputed Activation Storage 2786 isunoccupied throughout the entire Forward Pass and available for otheruses (e.g., other forward passes, other backward passes), therebyreducing the memory required.

Considering parallelization more generally, in some embodiments and/orusage scenarios, parallelizing a computation (e.g., neural networktraining) spreads the computation over separate computation unitsoperating simultaneously. In a model-parallel regime, separate unitssimultaneously evaluate a same neural network using distinct modelparameters. In a data-parallel regime, separate workers simultaneouslyevaluate distinct network inputs using the same formal model parameters.Some scaling techniques use fine-grained data parallelism across layersand among units in a cluster.

MBGD, in some embodiments and/or usage scenarios, improves accuracy of agradient estimate as a function of a mini-batch size, n. However,computation to perform MBGD for mini-batch size n is approximately equalto computation to perform SGD for n steps. In some situations, SGD for nsteps is more efficient than MBGD for a mini-batch size n byapproximately the square root of n. Thus, higher parallelism (e.g., asin MBGD) and higher efficiency (e.g., as in SGD) are sometimes mutuallyexclusive.

In some embodiments and/or usage scenarios, a deep neural network is ahigh-dimensional parameterized function, sometimes expressed as adirected acyclic graph. Back propagation techniques are sometimesexpressed by a cyclic graph. The cycle in the graph is a feedbackiteration. Gradients produced by a first full network evaluation changeweights used in a next iteration, because the iteration is a discreteapproximation of a continuous differential system. The discreteapproximation comprises an unbiased continuous-noise process withtime-varying statistics. The noise process provides regularization toenable the continuous system to model phenomena observed indiscrete-time learning systems. In the discrete case, regularization isprovided by a sampling procedure (e.g., SGD), by learning rate, and/orby other explicit mechanisms. A time-dependent noise process enablesusing a learning-rate schedule that erases local high-frequency contoursin parameter space. As a correct region is approached, regularization isreduced, leading, in some circumstances, to a better final solution.

CPGD, in a conceptual framework of an arbitrary feed-forward neuralnetwork, expresses all nodes as functions of time and applies functionalcomposition to formulate representations in terms of internal state andstimuli the internal state is subjected to. A factorization results withindividual layers as systems with independent local dynamics. Twodimensions are depth of the network and time evolution of parameters. Insome embodiments and/or usage scenarios implementing acceleration bymapping network layers to computational units separated in space, thereis latency communicating between the network layers. Thus, there is atime delay communicating between the layers. Some implementations ofCPGD are synchronous implementations that account for the time delays.

During CPGD processing, an activation vector and associated hiddenrepresentations are combined with model parameters at different timesteps during the forward pass of the activation vector. The differencebetween model parameters at different time steps versus a same time stepis not detectable by the activation vector going forward. Conceptuallyit is as if a fixed set of parameters from successive time steps wereused to form an aggregate parameter state that is then used forlearning.

There is a choice during the backward pass (e.g., delta propagation) touse either immediate parameters (e.g., weights) after updating or toretrieve historical parameters anchored to when the correspondingforward pass was performed. Deltas computed from the immediateparameters use updated information corresponding to a current parameterslope. Some embodiments and/or usage scenarios use immediate parameters.Some embodiments and/or usage scenarios use historical parameters.

Some implementations of CPGD use memory on an order similar to SGD.Reverse checkpoint (as described elsewhere herein) is usable with CPGD,such as to reduce memory usage. Some embodiments and/or usage scenariosof reverse checkpoint use immediate parameters (e.g., weights) torecompute activations. Some embodiments and/or usage scenarios ofreverse checkpoint use historical parameters to recompute activations.In some embodiments and/or usage scenarios using immediate parameters torecompute activations, a time disparity between parameters used forcomputing forward propagating activations and backward-propagatingdeltas is reduced in the aligning wavefronts.

Continuous propagation techniques are usable in conjunction withmini-batch style processing (e.g., MBGD). In some embodiments and/orusage scenarios, a subsequent batch is started before an immediatelypreceding batch is completed, conceptually similar to asynchronous SGD.Parameter inconsistency within the pipeline is limited to no more thanone batch boundary.

In some embodiments and/or usage scenarios, enabling data to streamthrough a neural network and to perform computations without a globalsynchronization boundary, enables extracting learning information nototherwise extracted. In some embodiments and/or usage scenarios, a lowerlearning rate dominates using larger batch sizes. In some embodimentsand/or usage scenarios, hidden activity and/or delta arcs areconceptually interpreted as individual vectors or alternatively batchmatrices. The batch matrices interpretation enables implementingtechniques as described herein directly on GPUs, CPUs, DSPs, FPGAs,and/or ASICs.

FIGS. 28A-28E illustrate various aspects of forward pass and backwardpass embodiments in accordance with SGD, MBGD, CPGD, and RCP processing.In the figures, two layers of neurons are illustrated, representingrespective layers of, e.g., a portion of a deep neural network. Invarious embodiments and/or usage scenarios, the deep neural networkcomprises thousands or more layers and thousands or more neurons perlayer. In various embodiments and/or usage scenarios, the first layer isan input layer receiving activations for training from an agent externalto the deep neural network. In various embodiments and/or usagescenarios, the second layer is an output layer where the forward passcompletes, and the backward pass begins. In various embodiments and/orusage scenarios, the first layer and the second layer are internallayers.

FIG. 28A and FIG. 28B respectively illustrate forward pass and backwardpass embodiments in accordance with SGD, MBGD, and CPGD, without RCP.The two layers are illustrated as Previous Layer 2801 and SubsequentLayer 2802. Previous Layer 2801 comprises Compute 2810 and Storage 2815.Subsequent Layer 2802 comprises Compute 2820 and Storage 2825. Compute2810 and Compute 2820 are examples of compute resources and Storage 2815and Storage 2825 are examples of storage resources.

FIGS. 28C-28E illustrate forward pass and backward pass embodiments inaccordance with SGD, MBGD, and CPGD, with RCP. The two layers areillustrated as Previous Layer 2803 and Subsequent Layer 2804. PreviousLayer 2803 comprises Compute 2830 and Storage 2835. Subsequent Layer2804 comprises Compute 2840 and Storage 2845. Compute 2830 and Compute2840 are examples of compute resources and Storage 2835 and Storage 2845are examples of storage resources.

Like-numbered elements in FIGS. 28A-28E have identical structure andoperation, although the compute resources produce different resultsdependent on differing inputs, and the storage resources store andsubsequently provide different values dependent on differing valuesstored. Other embodiments are envisioned with differing computeresources and/or differing storage resources usable for forward pass andbackward pass computation and storage. E.g., a backward pass uses atranspose weight storage not used by a forward pass. Other embodimentsare envisioned with differing compute and/or storage resources usablefor differing forward pass and backward pass implementations. E.g., anRCP-based embodiment uses an additional compute resource (notillustrated) than used for forward pass or backward pass processingwithout RCP.

Regarding FIG. 28A, Compute 2810 is enabled to perform computations,such as forward pass computations F 2811. Storage 2815 is enabled tostore activations, such as in A 2816. Storage 2815 is further enabled tostore weights, such as in W 2817. Compute 2820, F 2821, Storage 2825, A2826, and W 2827, are, in various embodiments and/or usage scenarios,substantially similar or identical in structure and/or operationrespectively to Compute 2810, F 2811, Storage 2815, A 2816, and W 2817.

In forward pass operation for SGD or MBGD, activation A_(1,t) 2881 isreceived by Previous Layer 2801 and stored in A 2816 (for later useduring the backward pass). A_(1,t) 2881 and a weight W_(1,t), previouslystored in W 2817, are then processed in accordance with F 2811 toproduce activation A_(2,t) 2882. A_(2,t) 2882 is then passed toSubsequent Layer 2802. Similarly to the Previous Layer, A_(2,t) 2882 isreceived by Subsequent Layer 2802 and stored in A 2826 (for later useduring the backward pass). A_(2,t) 2882 and a weight W_(2,t) previouslystored in W 2827 are then processed in accordance with F 2821 to produceactivation A_(3,t) 2883. A_(3,t) 2883 is then provided to a nextsubsequent layer (if present) for processing, and so forth, until theforward pass is complete, and the backward pass commences. If SubsequentLayer 2802 is the output layer, then the forward pass is completed andthe backward pass corresponding to the forward pass is initiated.

Regarding FIG. 28B, for clarity, elements of Compute 2810 and Compute2820 dedicated to forward pass processing (F 2811 and F 2821) areomitted. With respect to structure and operation illustrated anddescribed with respect to FIG. 28A, FIG. 28B illustrates that Compute2810 is further enabled to perform additional computations, such asbackward pass computations B 2812, and Compute 2820 is further enabledto perform additional computations, such as backward pass computations B2822. Storage 2815 is further enabled to store a computed weight, suchas in W 2818, and Storage 2825 is further enabled to store a computedweight, such as in W 2828. B 2822 and W 2828 are, in various embodimentsand/or usage scenarios, substantially similar or identical in structureand/or operation respectively to B 2812 and W 2818.

In backward pass operation for SGD or MBGD, delta Δ_(3,t) 2893 isreceived from the next subsequent layer (if present) during backwardpass processing. If Subsequent Layer 2802 is the output layer, thenSubsequent Layer 2802 computes delta Δ_(3,t) according to the deltarule, e.g., as a function of the difference between the output of theSubsequent Layer (e.g., the estimated output) and the training output(e.g., desired output). Δ_(3,t) 2893, the weight W_(2,t) previouslystored in W 2827, and the activation A_(2,t) previously stored in A2826, are then processed in accordance with B 2822 (e.g., in accordancewith the delta rule) to produce delta Δ_(2,t) 2892 and a new weightW_(2,t+1) that is then stored in W 2828 for use in a next forward pass.Δ_(2,t) 2892 is then passed to Previous Layer 2801. Similarly to theSubsequent Layer, delta Δ_(2,t) 2892, the weight W_(1,t) previouslystored in W 2817, and the activation A_(1,t) previously stored in A2816, are then processed in accordance with B 2812 to produce deltaΔ_(1,t) 2891 and a new weight W_(1,t+1) that is then stored in W 2818for use in the next forward pass. Δ_(1,t) 2891 is then passed to a nextprevious layer (if present) for processing, and so forth, until thebackward pass is complete, and a next forward pass commences. IfPrevious Layer 2801 is the input layer, then the backward pass iscomplete, and the next forward pass commences.

In SGD and MBGD (and unlike CPGD), the next forward pass is delayeduntil the previous backward pass completes, e.g., W 2817 and W 2827 arerespectively updated with W 2818 and W 2828 after W 2817 and W 2827 havebeen used for a same forward pass and a same corresponding backwardpass. Therefore, the next forward pass is performed using weights thatare from the same backward pass.

FIG. 28A, in addition to illustrating SGD and MBGD forward passprocessing, also illustrates CPGD forward pass processing. However,operation for CPGD is different compared to SGD and MBGD, in that weightupdates and the next forward pass are performed as soon as possible,rather than being delayed until completion of the previous backwardpass. E.g., W 2817 and W 2827 are respectively updated with W 2818 and W2828 as soon as possible. Therefore, the next forward pass has selectiveaccess to weights from prior iterations, and thus selectively producesactivations differing from those produced under the same conditions bySGD and MBGD.

More specifically, in Previous Layer 2801, A_(1,t) 2881 is received andstored in A 2816, identically to SGD and MBGD. A_(1,t) 2881 and a weightW_(1,t−k−j) previously stored in W 2817 are then processed in accordancewith F 2811 to produce activation A_(2,t) 2882. The weight W_(1,t−k−j)was produced and stored by a backward pass corresponding to a forwardpass preceding the instant forward pass by k-j forward passes. A_(2,t)2882 is then passed to Subsequent Layer 2802, and similarly to thePrevious Layer, A_(2,t) 2882 is received and stored in A 2826,identically to SGD and MBGD. A_(2,t) 2882 and a weight W_(2,t−k)previously stored in W 2827 are then processed in accordance with F 2821to produce activation A_(3,t) 2883. The weight W_(2,t−k) was producedand stored by a backward pass corresponding to a forward pass precedingthe instant forward pass by k forward passes. Note that the PreviousLayer and the Subsequent Layer, for processing of a same forward pass,use weights from different backward passes. As in SGD and MBGD, A_(3,t)2883 is then provided to a next subsequent layer (if present) forprocessing, and so forth, until the forward pass is complete, and thebackward pass commences. If Subsequent Layer 2802 is the output layer,then the forward pass is completed and the backward pass correspondingto the forward pass is initiated. In some embodiments and/or usagescenarios, the value of j is 0 and (k-j) and (k) are equal. In variousembodiments and/or usage scenarios, the Previous Layer and theSubsequent Layer simultaneously process one of: different forwardpasses, different backward passes, and a forward pass and a differentbackward pass.

FIG. 28B, in addition to illustrating SGD and MBGD backward passprocessing, also illustrates CPGD backward pass processing. Processingof the backward pass in CPGD is identical to that of SGD and MBGD.However, selected results (e.g., selected weights) are used earlier thanin SGD and MBGD. For example, W_(1,t−k−j), as produced by backward passt-k-j, and W_(1,t−k), as produced by backward pass t-k are used earlierthan in SGD and MBGD, e.g., forward pass t.

FIG. 28C illustrates an embodiment of forward pass processing of any ofSGD, MBGD, and CPGD, in combination with RCP. Compute 2830 and Storage2835, are, in various embodiments and/or usage scenarios, substantiallysimilar or identical in structure and/or operation respectively toCompute 2810 and Storage 2815. Compute 2840 and Storage 2845, are, invarious embodiments and/or usage scenarios, substantially similar oridentical in structure and/or operation respectively to Compute 2820 andStorage 2825, other than omission of storage for activations A 2826 ofStorage 2825 having no counterpart in Storage 2845.

In forward pass operation, with respect to Previous Layer 2803,activation A_(1,t) 2881 is received and processed in accordance withforward pass processing in Compute 2830, and stored in Storage 2835 asdescribed with respect to FIG. 28A. However, with respect to SubsequentLayer 2804, activation A_(2,t) 2882 is received, and processed inaccordance with forward pass processing in Compute 2840, but is notstored (instead it is recomputed in accordance with RCP during backwardpass processing).

FIG. 28D and FIG. 28E respectively illustrate first and second portionsof an embodiment of backward pass processing of any of SGD, MBGD, andCPGD, in combination with RCP. For clarity, elements of Compute 2830 andCompute 2840 dedicated to forward pass processing (F 2821) are omitted.With respect to structure and operation illustrated and described withrespect to FIG. 28C, FIG. 28D and FIG. 28E illustrate that Compute 2830is further enabled to perform additional computations, such as backwardpass computations B 2812, and Compute 2840 is further enabled to performadditional computations, such as backward pass computations B 2822.Storage 2835 is further enabled to store a computed weight, such as in W2818, and Storage 2845 is further enabled to store a computed weight,such as in W 2828, as well as a recomputed activation, such as in A2829.

In the first portion of the backward pass operation, activations notstored in the corresponding forward pass are recomputed. In SGD and MBGDscenarios, the recomputed activation is formulated in Previous Layer2803 by processing the activation stored from the forward pass in A 2816and weight stored in W 2817 in accordance with F 2811 to produceactivation A′_(2,t) 2884, that is then stored in A 2829 of SubsequentLayer 2804. Since SGD and MBGD delay weight updates and commencement ofa next forward pass until the forward pass and corresponding backwardpass are complete, A′_(2,t) 2884 is identical to the value discardedduring the forward pass, A_(2,t) 2882.

In a CPGD scenario, the recomputed activation is formulated according tothe same topology as the SGD and MBGD scenarios. However, CPGD performsupdates without delays and enables commencement of a next forward passwithout regard to completion of previous backward passes. Thus, a weightvalue stored at the time of the backward pass, e.g., in W 2817,according to embodiment and/or usage scenarios, selectively differs fromthe weight value stored during the corresponding forward pass. As aspecific example, in accordance with FIG. 28C, W 2817 stored W_(1,t−k−j)during the forward pass. However, during the backward pass, additionalweight updates have occurred, e.g., corresponding to m iterations, andnow W 2817 stores W_(1,t−k−j+m). Therefore, A′_(2,t) 2884 selectivelydiffers from the value discarded during the forward pass, A_(2,t) 2882.

In the second portion of backward pass operation, computation proceedsusing the recomputed activation. In SGD and MBGD scenarios, since therecomputed activation is identical to the discarded activation (e.g.,conceptually the value stored in A 2829 is identical to the value storedin A 2826), the backward processing produces results that are identicalto the results described with respect to FIG. 28B. E.g., deltas Δ′_(3,t)2896, Δ′_(2,t) 2895, and Δ′_(1,t) 2894 are identical, respectively, toΔ_(3,t) 2893, Δ_(2,t) 2892, and Δ_(1,t) 2891. In the CPGD scenario,since the recomputed activation selectively differs from the discardedactivation, the backward processing produces results that areselectively different from the results described with respect to FIG.28B. E.g., deltas Δ′_(3,t) 2896, A′_(2,t) 2895, and A′_(1,t) 2894 areselectively different, respectively, to Δ_(3,t) 2893, Δ_(2,t) 2892, andΔ_(1,t) 2891.

In some embodiments and/or usage scenarios, W 2817 is distinct from W2818 (as illustrated), and in some embodiments and/or usage scenarios, W2818 and W 2817 are a same portion of storage (not illustrated), suchthat saving a new value in W 2818 overwrites a previously saved value inW 2817. Similarly, W 2827 is variously distinct from or the same as W2828. In various embodiments and/or usage scenarios, A 2829 is variouslyimplemented to use fewer memory locations and/or use a same number ofmemory locations for a shorter time than A 2826.

In various embodiments and/or usage scenarios, activations and/orweights are implemented and/or represented by any one or more scalar,vector, matrix, and higher-dimensional data structures. E.g., any one ormore of A 2816, A 2826, A 2829, W 2817, W 2827, W 2818, and W 2828 areenabled to store any one or more of one or more scalars, one or morevectors, one or more matrices, and one or more higher-dimensionalarrays.

In various embodiments and/or usage scenarios, one or more elements ofPrevious Layer 2801 and Subsequent Layer 2802 are implemented byrespective PEs, e.g., a portion of PE 499 or similar elements of FIG. 4.E.g., PE 497 implements Previous Layer 2801 and PE 498 implementsSubsequent Layer 2802. Activation A_(2,t) 2882 and delta Δ_(2,t) 2892are communicated via East coupling 431. In some embodiments and/or usagescenarios, one or more elements of Previous Layer 2801 and SubsequentLayer 2802 are implemented by one or more of CPUs, GPUs, DSPs, andFPGAs.

In various embodiments and/or usage scenarios, all or any portions ofelements of F 2811, F 2821, B 2812, and B 2822 conceptually correspondto all or any portions of executions of instructions of Task SW on PEs260 of FIG. 2.

Floating-Point Operating Context and Stochastic Rounding Operation

In some scenarios, an FP computation results in a value that has moreprecision than is expressible by the number format. For example, withoutrounding, an FP multiply result is twice the precision of the inputs.Rounding is used to remove the additional precision, so, e.g., theresult is the same precision as the number format. The IEEE 754 standarddescribes five different (deterministic) rounding modes. Two modes roundto the nearest value, but with different rules for breaking a tie. Thedefault mode for some computing is round to nearest, with ties roundingto the nearest value with a ‘0’ in the ULP. A second mode is round tonearest with ties rounded away from zero. Three modes round according toa specific rule. Round to zero is equivalent to truncation and simplyremoves all bits after the ULP. Round to infinity is equivalent torounding up and rounding to negative infinity is equivalent to roundingdown. IEEE 754 FP arithmetic is sometimes performed in accordance withone of the five rounding modes.

In some neural network embodiments and/or usage scenarios, a trainingprocess iterates through many FP computations that form long dependencychains. For example, a single iteration includes many vector and/ormatrix FP operations that each has long dependency chains. For anotherexample, many iterations are performed, each dependent on a precedingone of the iterations, resulting in long dependency chains In somesituations, because of the long dependency chains, tiny biases inrounding compound across many computations to systematically biasresults, thus reducing accuracy, increasing training time, increasinginference latency, and/or reducing energy efficiency. In some scenariosand/or embodiments, use of stochastic rounding of FP results reduces thesystematic rounding bias, thereby improving accuracy, decreasingtraining time, decreasing inference latency, and/or increasing energyefficiency. In some scenarios and/or embodiments, rounding is performedon results of dependent FP operations (e.g. FP multiply-accumulateoperations), and the rounded results are then fed back into a subsequentdependent FP operation, resulting in long dependency chains of roundedoperations/results.

In some circumstances, performing stochastic rounding enables retainingsome precision that would otherwise be lost if performing non-stochastic(e.g. deterministic) rounding. For example, consider a scenario with aneural network comprising a layer with thousands or millions ofparameters, each parameter represented by a floating-point number withan N-bit mantissa. If the average magnitude of the parameter updates issmall (e.g., 10% of updates are represented by an N+1-bit mantissa, andthe remainder are even smaller), then without stochastic rounding theparameter updates would be rounded to zero and no learning would occur.With stochastic rounding, approximately 10% of the weights would beupdated and learning would occur, essentially recovering some numericalprecision lost by the N-bit mantissa, and thereby improving the latencyof training the neural network and/or improving the accuracy of thetrained neural network.

In some circumstances, neural network computations are conceptuallystatistical, and performing stochastic rounding instead ofnon-stochastic rounding enables effectively higher precision than wouldotherwise be possible in view of a particular FP precision. The improvedprecision of stochastic rounding enables smaller and morepower-efficient compute logic (e.g., FPUs) and smaller and morepower-efficient storage (e.g., latches, registers, and memories), thusenabling higher performance, lower latency, more accurate, and/or morepower-efficient systems for training neural networks and performinginference with trained neural networks.

In various embodiments and/or usage scenarios, stochastic rounding isimplemented at least in part via one or more PRNGs. An example of a PRNGis an RNG that deterministically generates a pseudo-random sequence ofnumbers, determined by an initial seed value. An LFSR is an example of aPRNG. Various PRNGs are implemented with LFSRs of varying length withrespect to the number of bits of generated random numbers. For a firstexample, a 3-bit PRNG is implemented with a 3-bit LFSR. For a secondexample, a 32-bit LFSR is used to implement a 3-bit PRNG, such as byusing the three LSBs of the LFSR as a 3-bit PRNG. Throughout thedescription herein, the term random number generator (RNG) will beunderstood to mean a pseudo-random number generator (PRNG), unlessotherwise explicitly specified.

The IEEE 754 standard describes multiple floating-point data formats.Each data format comprises a sign bit, a mantissa, and a biasedexponent. The biased exponent is the exponent plus an exponent bias.Each IEEE 754 floating-point number format specifies an exponent bias,e.g., the 16-bit half-precision format specifies an exponent bias of 15,enabling representation of an (un)biased exponent from −15 up to 16.Thus, about half of the numbers representable via the IEEE 754half-precision format have a magnitude less than one and half have amagnitude greater than one. In some neural networks, data values (e.g.,inputs, activations) are normalized (e.g., to the average data value, orto the unit interval [0,1]) and it is desirable to use a differentexponent bias, e.g., an exponent bias where more of the representablenumbers have a magnitude less than one and a lower maximum value (e.g.,a maximum value of six, such as six standard deviations above the mean,or a maximum value of one). In some scenarios and/or embodiments, aprogrammable exponent bias enables improving accuracy, decreasingtraining time, decreasing inference latency, and/or increasing energyefficiency.

In some embodiments, a custom floating-point number format enables adifferent number of bits for the mantissa and exponent, compared to IEEE754 formats. For example, a custom 16-bit floating-point number formatcomprising a sign bit, a six-bit biased exponent, and a nine-bitmantissa is the same number of bits as half-precision but enablesrepresenting a wider range of numbers via the larger biased exponent. Insome scenarios and/or embodiments (e.g., summing many small numbers), alarger biased exponent enables improving accuracy, decreasing trainingtime, decreasing inference latency, and/or increasing energy efficiency.In some embodiments, a custom FP number format is combined withprogrammable exponent bias.

The IEEE 754 standard uses the maximum biased exponent to representinfinite (e.g., numbers with a magnitude too large to represent) orspecial numbers (e.g., NaN), and specifies processing of numbers withthe maximum biased exponent differently than ‘normal’ numbers withother-then the maximum biased exponent. This enables handling certainexceptional conditions (e.g., computing with a too large number), butreduces available representations in the IEEE data formats (e.g., bylimiting the use of the maximum biased exponent). In some neuralnetworks, numbers of a magnitude otherwise too large to represent arerepresented as the maximum magnitude number (e.g., instead of infinity).In some scenarios and/or embodiments, the maximum magnitude numbercomprises the maximum biased exponent. In some scenarios and/orembodiments, FP numbers with a maximum biased exponent are processed asnormal numbers, e g, infinities and NaNs are not supported, therebyenabling a larger biased exponent by enabling use of the maximum biasedexponent for normal computations. In some scenarios and/or embodiments(e.g., summing many small numbers), a larger biased exponent enablesimproving accuracy, decreasing training time, decreasing inferencelatency, and/or increasing energy efficiency. In some scenarios and/orembodiments, processing an FP number with a maximum biased exponent as anormal number is combined with clip to maximum rounding, so that numbersthat are otherwise of too large a magnitude to represent are rounded tothe largest representable number.

The IEEE 754 standard uses the zero biased exponent to represent‘subnormal’ numbers (e.g., numbers with a magnitude too small tootherwise represent). This enables handling certain exceptionalconditions (e.g., computing with a too small number), but reducesavailable representations in the IEEE data formats (e.g., by limitingthe use of the zero biased exponent). In some neural networks, numbersof a magnitude otherwise too small to represent are represented as thesmallest magnitude number (e.g., instead of a subnormal). In somescenarios and/or embodiments, the smallest magnitude number comprisesthe zero biased exponent. In some scenarios and/or embodiments, FPnumbers with a zero biased exponent are processed as normal numbers,e.g., subnormal numbers are not supported, thereby enabling a largerbiased exponent range by enabling use of the zero biased exponent fornormal computations. In some scenarios and/or embodiments (e.g., summingmany small numbers), a larger biased exponent range enables improvingaccuracy, decreasing training time, decreasing inference latency, and/orincreasing energy efficiency. In some neural networks, numbers of amagnitude otherwise too small to represent are treated as zero (e.g.,instead of a subnormal). In some scenarios and/or embodiments,processing an FP number with a zero biased exponent as a normal numberis combined with one or more of: round to zero rounding andflush-to-zero behavior, so that subnormal numbers are processed as zero.

FIG. 29 illustrates selected details of Processor 2900 comprising FPU2901 and enabled to optionally and/or selectively perform stochasticrounding for floating-point operations that produce floating-point,integer, and/or fixed-point results. In some embodiments and/or usagescenarios, Processor 2900 and FPU 2901 are enabled to optionally operatein accordance with a programmable exponent bias, a custom FP numberformat, a zero biased exponent is normal mode, and/or a maximum biasedexponent is normal mode. In some embodiments, Processor 2900 comprisesor is a portion of a deep learning accelerator, CPU, a GPU, an ASIC, oran FPGA. In various embodiments, any one or more of a deep learningaccelerator, a CPU, a GPU, an ASIC, and an FPGA incorporates techniquesas illustrated by FIG. 29.

Various embodiments comprise a plurality of instances of Processor 2900and/or variations thereof. In various embodiments, a two-dimensional (ormore-dimensional) array comprises a plurality of the instances ofProcessor 2900. In various embodiments, the array dimensionality isimplemented as any one or more of a physical arrangement, a logicalarrangement, a virtual arrangement, and a communication arrangement. Invarious usage scenarios, all or any portions of the instances performall or any portions of operations that are long dependency chains. Invarious usage scenarios, the instances communicate with each other inaccordance with the long dependency chains, such as to communicateresults of computation, partial computations, intermediate calculations,feedback values, and so forth. In various usage scenarios, the longdependency chains comprise long dependency chains of FP computations. Invarious usage scenarios, the long dependency chains are performed whollyor in part to train one or more neural networks and/or to performinferences with respect to one or more trained neural networks. Invarious usage scenarios, rounding bias is reduced in at least some ofthe long dependency chains (or one or more portions thereof) by usingstochastic rounding such as enabled by random number informationprovided by the respective instance of RNGs 2921 included in eachinstance of Processor 2900. In some embodiments, Processor 2900 is aportion of a neural network accelerator. In various usage scenarios, oneor more of accuracy, performance, and energy-efficiency is improved byoperating in accordance with a programmable exponent bias and/or acustom FP number format, sometimes in conjunction with a zero biasedexponent or maximum biased exponent in normal mode.

FPU 2901 comprises FP control and execution logic such as InstructionDecode Logic 2920, RNGs 2921, FP Control Register 2925, Multiplier 2911,Accumulator 2912, Normalizer 2913, and Exponent DP 2915, as well asrounding logic such as N-bit Adder 2922 and Incrementer 2914. Processor2900 comprises Instruction Decode Logic 2920 that is enabled to receiveInstruction 2950 and decode Instruction 2950 into operations executed byFPU 2901. FIG. 30A illustrates selected details of Instruction 2950. Invarious embodiments, Processor 2900 comprises one or more RNGs 2921, andInstruction Decode Logic 2920 is coupled to the one or more RNGs 2921.In other embodiments, Processor 2900 comprises FPU 2901, and FPU 2901comprises one or more RNGs 2921. In various embodiments, one or more ofRNGs 2921 comprises one or more LFSRs.

In various embodiments, RNGs 2921 are initialized with seed values byconfiguration instructions, are readable by configuration instructions,and/or are writable by configuration instructions. In some usagescenarios, RNGs 2921 are managed to enable time-sharing of acomputational system implemented in part by Processor 2900. For example,RNGs 2921 are initialized as part of initializing a first neural networkcomputation, and after a portion of the first computation is completed,RNGs 2921 are read and saved in a first portion of non-volatile memory(not illustrated). Then, RNGs 2921 are initialized as part ofinitializing a second neural network computation, and after a portion ofthe second computation is completed, RNGs 2921 are read and saved in asecond portion of the memory. Then, RNGs 2921 are written using thesaved values from the first portion of the memory, and the firstcomputation is resumed. In some embodiments, PRNGs enable deterministicrandom number generation which is advantageous in some usage scenarios,e.g., enabling reproducible computations. In various embodiments, RNGs2921 comprise an entropy source that is not pseudo-random (e.g., trulyrandom or quasi-random). In some embodiments, RNGs 2921 comprises onerandom number generator (e.g., a single PRNG, a single PRNG comprising aLFSR). In some embodiments, RNGs 2921 comprises a plurality of PRNGs. Afirst one of the RNGs is initialized as part of initializing a firstneural network computation and a second one of the RNGs is initializedas part of initializing a second neural network computation that to beperformed in parallel with the first neural network computation. Thefirst and the second ones of RNGs are enabled to operate simultaneously,thereby enabling multiple neural network computations to be performedusing deterministic random number generation.

Instruction Decode Logic 2920 is coupled to FPU 2901 and communicates anoperation to be performed by FPU 2901, such as an FP multiply-accumulateoperation with optional stochastic rounding, an FP multiply operationwith optional stochastic rounding, an integer-to-FP data conversion withoptional stochastic rounding, and so forth. The operation to beperformed is specified by OpCode Bits 3023 of Instruction 2950 (See FIG.30A). FPU 2901 comprises execution hardware that performs theoperations. In various embodiments, Multiplier 2911 and Accumulator 2912are coupled to various data storage locations such as registers, flops,latches, bypass networks, caches, explicitly addressed RAMs/DRAMs/SRAMs,and accumulation resources. Multiplier 2911 receives as operands Src A2951 and Src B 2952 from the data storage locations specified by SourceBits 3024 of Instruction 2950 (see FIG. 30A) and performs an FP multiply(without normalizing and without rounding) of the operands to generateIntermediate Result 2953 (having biased exponent and mantissa portions).Accumulator 2912 is coupled to Multiplier 2911 and the data storagelocations. Accumulator 2912 receives as operands Intermediate Result2953 from Multiplier 2911 and Src C 2954 from the data storage locationspecified by Source Bits 3024 of Instruction 2950, and performs an FPadd (without normalizing and without rounding) of the operands togenerate Mantissa 2955 (as well as a biased exponent provided toExponent DP 2915).

Referring to FIG. 29, FIG. 30C, and FIG. 30D, Normalizer 2913 is coupledto Accumulator 2912 and receives Mantissa 2955 from Accumulator 2912.According to usage scenario, Mantissa 2955 has zero or moremore-significant zero bits, illustrated by Leading Zeros 2955.1. Theremainder of less significant bits of Mantissa 2955 is denoted as OtherBits 2955.2. Normalizer 2913 normalizes Mantissa 2955 by detectingLeading Zeros 2955.1 and shifting Other Bits 2955.2 to the left,removing Leading Zeros 2955.1 to produce Normalized Mantissa 2956comprising Mantissa Bits Subject to Rounding 2958 and N Most SignificantLower Bits 2957.1. Normalizer 2913 is coupled to Incrementer 2914 andN-bit Adder 2922. Normalizer 2913 provides Mantissa Bits Subject toRounding 2958 to Incrementer 2914, and N Most Significant Lower Bits2957.1 to N-bit Adder 2922. In various embodiments, the bit widths ofMantissa Bits Subject to Rounding 2958 and Stochastically RoundedMantissa 2964 vary according to FP data format and/or FP data precision.For example, the bit widths of Mantissa Bits Subject to Rounding 2958and Stochastically Rounded Mantissa 2964 are 10 bits forcustom-precision, 11 bits for half-precision, 24 bits forsingle-precision, and 53 bits for double-precision.

Instruction Decode Logic 2920 is enabled to select a random numberresource of RNGs 2921. Instruction Decode Logic 2920 decodes RoundingMode Bits 3021 to determine a rounding mode associated with processingof the operation (the operation being specified by OpCode Bits 3023). IfRounding Mode Bits 3021 specify stochastic rounding, then InstructionDecode Logic 2920 decodes RNG Bits 3022 to generate RNG Selector 2961.RNGs 2921, in response to RNG Selector 2961, provide N-bit Random Number2962. In various embodiments, RNGs 2921, further in response to RNGSelector 2961, advance the selected random number resource to produce anext random number. For example, RNGs 2921 implements four random numberresources specified, selected, and identified respectively as 0, 1, 2,and 3. Each random number resource comprises a separate LFSR. Inresponse to RNG Bits 3022 having a value of ‘1’, Instruction DecodeLogic 2920 provides a value of ‘1’ on RNG Selector 2961. In response toRNG Selector 2961 being ‘1’, RNGs 2921 provides the value of LFSR ‘1’ asN-bit Random Number 2962, and subsequently advances the state of LSFR‘1’ to a next state. In various embodiments, one or more random numberresources of RNGs 2921 are usable as source operands of instructions,such as any more of Src A 2951, Src B 2952, and Src C 2954, therebyproviding random numbers as input data for the instructions.

In some embodiments, N-bit Adder 2922 is an integer adder that isenabled to receive and sum two inputs: N Most Significant Lower Bits2957.1 and N-bit Random Number 2962. N-bit Adder 2922 provides a carryout of the sum as Carry Bit 2963. Incrementer 2914 receives MantissaBits Subject to Rounding 2958 and Carry Bit 2963. Incrementer 2914provides an output that is a conditional increment of Mantissa BitsSubject to Rounding 2958 as Stochastically Rounded Mantissa 2964. IfCarry Bit 2963 is asserted, then Incrementer 2914 provides an increment(starting at ULP 3002.1) of Mantissa Bits Subject to Rounding 2958 asStochastically Rounded Mantissa 2964. If Carry Bit 2963 is de-asserted,then Incrementer 2914 provides Mantissa Bits Subject to Rounding 2958without change as Stochastically Rounded Mantissa 2964. In variousembodiments, the bit width of Incrementer 2914 varies to accommodate thebit width of Mantissa Bits Subject to Rounding 2958. For example, if thebit width of Mantissa Bits Subject to Rounding 2958 is 11 bits(half-precision), then Incrementer 2914 is also 11 bits. As anotherexample, if the bit width of Mantissa Bits Subject to Rounding 2958 is10 bits (custom-precision), then Incrementer 2914 is also 10 bits. Invarious embodiments, N is 3, the N Most Significant Lower Bits 2957.1comprises 3 bits, the N-bit Random Number 2962 comprises 3 random bits,and the N-bit Adder 2922 comprises a 3-bit adder. In various otherembodiments, N is variously 4, 5, 7, or any integer number.

Exponent DP 2915 is an FP exponent data path that adjusts, in accordancewith normalization information received from Normalizer 2913, anexponent received from Accumulator 2912. In some embodiments and/orusage scenarios, Exponent DP 2915 receives rounding information (such asstochastic rounding information) from Incrementer 2914 and furtheradjusts the biased exponent accordingly, producing StochasticallyRounded Biased Exponent 2965. Stochastically Rounded Biased Exponent2965 and Stochastically Rounded Mantissa 2964 taken together form acomplete FP result, suitable, for example, for storage for later use, orfor feedback to any of Src A 2951, Src B 2952, and Src C 2954 as aninput operand for subsequent operations.

In some embodiments, Exponent DP 2915 is enabled to operate oncustom-precision biased exponents (e.g., six-bit biased exponents, inaccordance with FP Control Register 2925 element Large Exponent 2925.7).In various embodiments, Exponent DP 2915 is enabled to operate inaccordance with a programmable exponent bias (e.g., in accordance withFP Control Register 2925 element Exponent Bias 2925.6 via couplingExponent Bias 2970). In some embodiments, Exponent DP 2915 is enabled tooperate with maximum and/or zero biased exponents as normal numbers(e.g., in accordance with FP Control Register 2925, elements Max BiasedExponent Normal 2925.4 and Zero Biased Exponent Normal 2925.5,respectively) and is enabled to round in accordance with clip to maximumrounding (e.g., in accordance with FP Control Register 2925 elementStatic Rounding Mode Bits 2925.1). In some embodiments, Exponent DP 2915is enabled to flush subnormal results to zero (e.g., in accordance withFP Control Register 2925 element FTZ 2925.3). In some embodiments and/orusage scenarios, Stochastically Rounded Biased Exponent 2965 is relativeto a programmable exponent bias.

In various embodiments, Processor 2900 comprises FP Control Register2925. In some embodiments, FPU 2901 comprises FP Control Register 2925.In some embodiments, FP Control Register 2925 specifies that all or anyportions of operations (such as all FP multiplies and all FPmultiply-accumulates) are performed using a specified rounding mode(e.g., a stochastic rounding mode of a plurality of rounding modes). Invarious embodiments, rounding mode information from Instruction 2950overrides the specified rounding mode from FP Control Register 2925(such as on an instruction-by-instruction basis). In some embodiments,FP Control Register 2925 provides random number resource selectioninformation specifying that all stochastically rounded operations areperformed using a specified one or more random number resources of RNGs2921. In various embodiments, random number resource selectioninformation from Instruction 2950 overrides the random number resourceselection information from FP Control Register 2925. In variousembodiments, FP Control Register 2925 is memory-mapped and accessedusing instructions that access memory, e.g. a memory store instruction.In some other embodiments, FP Control Register 2925 is accessed usinginstructions that access registers and/or control registers, e.g., aload control register instruction.

The partitioning in FIG. 29 is merely exemplary. In various embodiments,two or more elements of FIG. 29 are implemented as a single unit. Forexample, in some embodiments, Multiplier 2911 and Accumulator 2912 areimplemented as a fused FP multiplier-accumulator.

As illustrated, FPU 2901 is enabled to perform FP multiply-accumulateoperations with optional stochastic rounding In some embodiments,additional hardware (not illustrated) enables FPU 2901 to performadditional FP operations with optional stochastic rounding, such asaddition, subtraction, multiplication, division, reciprocal, comparison,absolute value, negation, maximum, minimum, elementary functions, squareroot, logarithm, exponentiation, sine, cosine, tangent, arctangent,conversion to a different format, and conversion from/to integer.

In various embodiments and/or usage scenarios, Processor 2900 hashardware logic to fetch a stream of instructions from an instructionstorage element, providing the fetched instructions to InstructionDecode Logic 2920 as respective instances of Instruction 2950. Invarious embodiments, the instruction storage element implementsnon-transitory media, such as computer readable medium such as acomputer readable storage medium (e.g., media in an optical and/ormagnetic mass storage device such as a disk, or an integrated circuithaving non-volatile storage such as flash storage).

FIG. 30A illustrates selected details of floating-point Instruction 2950that optionally specifies stochastic rounding. Instruction 2950comprises several bit fields. In various embodiments and/or usagescenarios, Instruction 2950 comprises any zero or more of OpCode Bits3023, Source Bits 3024, Dest Bits 3025, Rounding Mode Bits 3021, and/orRNG Bits 3022. OpCode Bits 3023 specifies one or more FP operations tobe executed, such as any one or more of addition, subtraction,multiplication, division, reciprocal, comparison, absolute value,negation, maximum, minimum, elementary functions, square root,logarithm, exponentiation, sine, cosine, tangent, arctangent, conversionto a different format, conversion from/to integer, andmultiply-accumulate. In various embodiments, OpCode Bits 3023 optionallyspecifies one or more datatypes associated with the operations, such asany one or more of integer, floating-point, half-precisionfloating-point, single-precision floating-point, and double-precisionfloating-point datatypes. Source Bits 3024 optionally specifies one ormore source operands corresponding to locations of input data for theoperations. Dest Bits 3025 optionally specifies one or more destinationoperands corresponding to locations for storage of output data of theoperations. In various embodiments, source and/or destination operandsare various storage locations, such as registers, flops, latches, bypassnetworks, caches, explicitly addressed RAMs/DRAMs/SRAMs, andaccumulation resources. In various embodiments, source and/ordestination operands are various other elements, such as elements of abypass network.

Rounding Mode Bits 3021 optionally specifies one or more rounding modesto use when processing the operations, such as stochastic rounding, anyIEEE 754 standard rounding, and any other rounding modes. RNG Bits 3022optionally specifies one or more random number resources of RNGs 2921 touse when processing the operations, such as when performing stochasticrounding.

FIG. 30B illustrates selected details of FP Control Register 2925associated with controlling stochastic rounding, programmable exponentbias, and floating-point computation variations. In various embodiments,FP Control Register 2925 comprises a bit field Static Rounding Mode Bits2925.1 that specifies a rounding mode to use for operations performed byFPU 2901. In various embodiments, Static Rounding Mode Bits 2925.1specifies a stochastic rounding mode or one of five IEEE 754 standardrounding modes (the five IEEE 754 rounding modes are deterministicrounding modes that depend only the input data to be rounded). In somescenarios, all operations performed by FPU 2901 use the rounding modespecified by Static Rounding Mode Bits 2925.1. In some embodiments,Static Rounding Mode Bits 2925.1 is set by a configuration instruction.For example, a configuration instruction sets Static Rounding Mode Bits2925.1 to specify a stochastic rounding mode, and all subsequentlyexecuted operations use stochastic rounding until Static Rounding ModeBits 2925.1 are changed to specify a different rounding mode. In someembodiments and/or usage scenarios, Rounding Mode Bits 3021 ofInstruction 2950 override Static Rounding Mode Bits 2925.1 of FP ControlRegister 2925, such as on a per-instruction basis. In some embodiments,Static Rounding Mode Bits 2925.1 specifies one or more saturatedrounding modes that round any result greater in magnitude than themaximum magnitude to the maximum magnitude (instead of to infinity). Invarious embodiments, the one or more saturated rounding modes comprise adeterministic saturated rounding mode and a stochastic saturatedrounding mode.

In some embodiments, FP Control Register 2925 comprises bit field FTZ2925.3 that controls behavior of subnormal FP numbers. When FTZ 2925.3is a first value (e.g., 1), FPU 2901 flushes subnormal results of anoperation to zero. When FTZ 2925.3 is a second value (e.g., 0), FPU 2901processes subnormal numbers in accordance with IEEE 754. In variousembodiments, FP Control Register 2925 comprises bit fields Max BiasedExponent Normal 2925.4 and/or Zero Biased Exponent Normal 2925.5. WhenMax Biased Exponent Normal 2925.4 is a first value (e.g., 0), FP valuescomprising the maximum biased exponent represent infinity and NaN (e.g.,in accordance with IEEE 754). For example, operations performed by FPU2901 that overflow the FP representation return infinity, whileotherwise retaining behavior of the rounding mode specified (e.g., byRounding Mode Bits 3021). When Max Biased Exponent Normal 2925.4 is asecond value (e.g., 1), FP values comprising the maximum biased exponentrepresent normal FP numbers, extending the representable range. In someembodiments, when Max Biased Exponent Normal 2925.4 is set to the secondvalue, a saturated rounding mode is enabled so that operations performedby FPU 2901 that overflow the FP representation return the maximumnormal magnitude value, instead of returning infinity, while otherwiseretaining behavior of the rounding mode specified (e.g., by RoundingMode Bits 3021). When Zero Biased Exponent Normal 2925.5 is a firstvalue (e.g., 0), some FP values comprising the zero biased exponentrepresent subnormal numbers (e.g., in accordance with IEEE 754). Forexample, operations performed by FPU 2901 that underflow the FPrepresentation return subnormal numbers, while otherwise retainingbehavior of the rounding mode specified (e.g., by Rounding Mode Bits3021). When Zero Biased Exponent Normal 2925.5 is a second value (e.g.,1), FP values comprising the zero biased exponent represent normalnumbers, extending the representable range. In some embodiments, whenZero Biased Exponent Normal 2925.5 is set to the second value, FTZ2925.3 is set to the first value so that operations performed by FPU2901 that underflow the FP representation return zero, while otherwiseretaining behavior of the rounding mode specified (e.g., by RoundingMode Bits 3021). In some embodiments, FP Control Register 2925 comprisesfield Large Exponent 2925.7 that specifies the size of the exponent fora 16-bit FP number. When Large Exponent 2925.7 is a first value (e.g.,0), 16-bit FP numbers are processed in accordance with a five-bitexponent and an 11-bit mantissa. When Large Exponent 2925.7 is a secondvalue (e.g., 1), 16-bit FP numbers are processed in accordance with asix-bit exponent and a10-bit mantissa. In some embodiments, FP ControlRegister 2925 comprises field Exponent Bias 2925.6 that specifies aprogrammable exponent bias for representing FP numbers. In variousembodiments, Exponent Bias 2925.6 is a six-bit field that is interpretedas a five-bit field (representing, without restriction, between 1 and30) for half-precision mode (e.g., Large Exponent 2925.7 set to 0) andinterpreted as a six-bit field (representing, without restriction,between 1 and 62) for large exponent mode (e.g., Large Exponent 2925.7set to 1).

In various embodiments, the number of random number resourcesimplemented by RNGs 2921 is respectively 1, 2, 4, and 7. In varioususage scenarios, respective groups of instructions specify (viarespective values in RNG Bits 3022 and/or Static RNG Bits 2925.2) to userespective ones of the random number resources of RNGs 2921. Forexample, the respective RNG Bits 3022 value in a first group ofinstructions is a same first value, specifying that all the instructionsin the first group use a same first random number resource of RNGs 2921for stochastic rounding. Continuing with the example, the respective RNGBits 3022 value in a second group of instructions is a same secondvalue, specifying that all the instructions in the second group use asame second random number resource of RNGs 2921 for stochastic rounding.For another example, preceding execution of a first group ofinstructions, Static RNG Bits 2925.2 is set by a first configurationinstruction to specify a first random number resource of RNGs 2921 forstochastic rounding. Continuing with the example, the first group ofinstructions is executed, in accordance with the first random numberresource. Then, preceding a second group of instructions, Static RNGBits 2925.2 is set by a second configuration instruction to specify asecond random number resource of RNGs 2921 for stochastic rounding.Continuing with the example, the second group of instructions isexecuted, in accordance with the second random number resource. In someembodiments, specification of which RNG to use for an instruction ispredetermined and/or implicit. E.g., in embodiments with a single RNG,the single RNG is used without reference to RNG Bits 3022 or Static RNGBits 2925.2.

There are no requirements on arrangement in storage or execution withrespect to instructions of the groups. In various embodiments and usagescenarios, instructions in the first group are contiguous with respectto each other in program storage and/or execution order, are notcontiguous with respect to each other in program storage and/orexecution order, and are variously arranged with respect to each otherand other instructions, such as intermixed with one or more instructionsof any other groups of instructions, and similarly for the second groupand any other groups of instructions. In some embodiments and/or usagescenarios, using a same random number resource of a group ofinstructions improves determinism and/or reproducibility of execution.

In some scenarios where random number resource selection variesrelatively frequently, instructions specify that random number resourceselection is via respective values in RNG Bits 3022, and the respectivevalues optionally vary from one instruction to the next. In somescenarios where random number selection varies relatively infrequently,instructions specify that random number resource selection is via StaticRNG Bits 2925.2, and the value therein is held constant for severalinstructions.

FIG. 30C illustrates selected details of Mantissa 2955 (a mantissa of aresult of a floating-point operation, subject to normalization androunding), with the MSB on the left side and the LSB on the right side.In some embodiments, Mantissa 2955 has more bits than the mantissa ofthe FP data format used by the FP operation. In some embodiments,Mantissa 2955 of a half-precision multiply-accumulate operation is 45bits, and Mantissa 2955 is normalized and rounded to a 16-bitrepresentation with an 11-bit mantissa. Mantissa 2955 as illustrated hastwo fields, zero or more contiguous Leading Zeros 2955.1 and remainingbits Other Bits 2955.2 (having a most significant bit of value ‘1’).

FIG. 30D illustrates selected details of Normalized Mantissa 2956 (amantissa of a result of a floating-point operation after normalization,and subject to rounding), with the MSB on the left side and the LSB onthe right side. Normalized Mantissa 2956 as illustrated has two fields,Mantissa Bits Subject to Rounding 2958 and Lower Bits 3003. The MSB ofNormalized Mantissa 2956 is a leading ‘1’ (although in some embodimentsthe leading ‘1’ is not explicitly stored). The LSB of Mantissa BitsSubject to Rounding 2958 is ULP 3002.1. Lower Bits 3003 are bits lesssignificant than ULP 3002.1. Lower Bits 3003 as illustrated has twofields, N Most Significant Lower Bits 2957.1 and Least Significant LowerBits 3003.2. In various embodiments, stochastic rounding enables the NMost Lower Significant Bits 2957.1 to probabilistically influencerounding of Mantissa Bits Subject to Rounding 2958 starting at ULP3002.1. In some embodiments and/or usage scenarios, theprobabilistically influencing enables reducing systematic rounding biasin computations that comprise portions of long dependency chains, suchas long dependency chains associated with neural network computations.

FIG. 30E illustrates selected details of an embodiment of afloating-point number datatype, e.g., as stored in memory, a register,or as communicated via a fabric vector. In various embodiments, Src A2951, Src B 2952, and Src C 2954 of FIG. 29 are formatted in accordancewith FIG. 30E. In some embodiments, Stochastically Rounded BiasedExponent 2965 and Stochastically Rounded Mantissa 2964 of FIG. 29 arerespective examples of Biased Exponent 3052 and Mantissa 3051. In someembodiments, any one or more of various instances of 16-bit FP data,e.g., Sparse Data 1322 of FIG. 13A, Dense Data 1343.1, and Dense Data1343.2 of FIG. 13B are formatted in accordance with FIG. 30E. In someembodiments, any one or more of various instances of 32-bit FP data,e.g., Dense Data 1343.1 and Dense Data 1343.2 collectively are formattedin accordance with FIG. 30E. In some embodiments, all or any portions ofFIG. 31 are performed with floating-point numbers formatted inaccordance with FIG. 30E.

FP Number 3050 comprises a sign field (Sign 3051), a biased exponentfield (Biased Exponent 3052), and a mantissa field (Mantissa 3053). Invarious embodiments, Sign 3051 comprises a sign bit. In variousembodiments, Mantissa 3053 comprises one of: 23 bits (e.g., IEEE 754single-precision), 10 bits (e.g., IEEE 754 half-precision), and 9 bits(e.g., a custom 16-bit FP format). In some embodiments, Biased Exponent3052 comprises one of: 8 bits (e.g., IEEE 754 single-precision), 6 bits(e.g., IEEE 754 half-precision), and 5 bits (e.g., the custom 16-bit FPformat). FP Number 3050 represents a floating-point number in accordancewith an exponent bias (e.g., Exponent Bias 2925.6), and modesdetermining treatment of zero and maximum biased exponents (e.g., asindicated by Max Biased Exponent Normal 2925.4 and Zero Biased ExponentNormal 2925.5). When the floating-point number represented by FP Number3050 is normal, the sign and mantissa of the floating-point number arerespectively Sign 3051 and Mantissa 3052. The exponent of thefloating-point number is Biased Exponent 3052 plus the exponent bias.

FIG. 31 illustrates a flow diagram of selected details of Processor 2900executing a floating-point instruction with optional stochasticrounding. For exposition, the instruction is an FP multiply-accumulateinstruction. In other embodiments and/or usage scenarios, theinstruction is any FP instruction such as addition, subtraction,multiplication, division, reciprocal, comparison, absolute value,negation, maximum, minimum, elementary functions, square root,logarithm, exponentiation, sine, cosine, tangent, arctangent, conversionto a different format, and conversion from/to integer.

Processing of Instruction 2950 begins in action 3100. In action 3110,Processor 2900 decodes Instruction 2950 and various specifiers therein.The specifiers include an operation specifier (such as specifying an FPmultiply-accumulate operation via a specific encoding in OpCode Bits3023). In various embodiments, the FP multiply-accumulate instructionspecifies one of half-, single-, and double-precision data andoperations. In some embodiments, the data and operation precision arespecified by OpCode Bits 3023, and in other embodiments the data andoperation precision are specified by a separate bitfield in Instruction2950 (not illustrated).

In action 3120, Multiplier 2911 performs an FP multiplication of Src A2951 and Src B 2952, producing Intermediate Result 2953 as a result(having exponent and mantissa portions). In some embodiments and/orusage scenarios, Src A 2951, Src B 2952, and Intermediate Result 2953have exponents relative to a programmable exponent bias (e.g., inaccordance with FP Control Register 2925 element Exponent Bias 2925.6).Accumulator 2912 then performs an FP add of Intermediate Result 2953 andSrc C 2954, producing Mantissa 2955 as a result (as well as an exponentprovided to Exponent DP 2915). In various embodiments and/or usagescenarios, Exponent DP 2915 operates in accordance with a programmableexponent bias, e.g. Exponent Bias 2925.6 such as provided via ExponentBias 2970. In action 3130, Normalizer 2913 normalizes Mantissa 2955,detecting Leading Zeros 2955.1 (if any) and shifting Other Bits 2955.2to the left, removing Leading Zeros 2955.1 to produce NormalizedMantissa 2956.

In action 3140, Processor 2900 determines the rounding mode, e.g., bydecoding Rounding Mode Bits 3021. If Rounding Mode Bits 3021 specifies astochastic rounding mode 3142, then flow proceeds to action 3160. IfRounding Mode Bits 3021 specifies other-than a stochastic rounding mode(e.g. round to nearest even) 3141, then flow proceeds to action 3150. Inaction 3150, FPU 2901 deterministically rounds (e.g. without stochasticrounding) according to the specified rounding mode, and flow proceeds toaction 3198.

In action 3160, Processor 2900 selects a random number resource of RNGs2921 (e.g., based on decoding RNG Bits 3022). In some embodiments, arandom number resource of RNGs 2921 is selected based on Static RNG Bits2925.2. The selected random number resource is provided as N-bit RandomNumber 2962. In action 3170, N-bit Random Number 2962 and N MostSignificant Lower Bits 2957.1 are added together (integer addition) byN-bit Adder 2922.

In action 3180, subsequent flow is conditionally dependent on whetherthe addition performed by N-bit Adder 2922 produces a carry (Carry Bit2963 is asserted). If so 3182, then flow proceeds to action 3190. If not3181, then Mantissa Bits Subject to Rounding 2958 is provided withoutchange (such as by a pass-through function of Incrementer 2914) asStochastically Rounded Mantissa 2964, and flow proceeds to action 3198.In action 3190, Incrementer 2914 provides an increment (starting at ULP3002.1) of Mantissa Bits Subject to Rounding 2958 as StochasticallyRounded Mantissa 2964. Flow then proceeds to action 3198, whereStochastically Rounded Biased Exponent 2965 (e.g., relative to aprogrammable exponent bias) and Stochastically Rounded Mantissa 2964 arecollectively provided to a destination in accordance with thedestination operand specifier (Dest Bits 3025). Processing of theinstruction is then complete at action 3199.

In some embodiments and/or usage scenarios, action 3170 is conceptuallya mechanism to compare N-bit Random Number 2962 and N Most SignificantLower Bits 2957.1 to determine whether to round up (3182) or round down(3181). By using N-bit Random Number 2962 as a comparison source,probability of the round up/down decision is equal to the fractionrepresented by N Most Significant Lower Bits 2957.1 (e.g., theprobability of rounding away from zero is the fraction represented by NMost Significant Lower Bits 2957.1), which enables unbiased rounding. Insome embodiments, Least Significant Lower Bits 3003.2 is ignored whenperforming stochastic rounding In some embodiments, the LSB of N MostSignificant Lower Bits 2957.1 is replaced with a logical OR of what NMost Significant Lower Bits 2957.1 would otherwise be and one or morebits of Least Significant Lower Bits 3003.2.

In some embodiments and/or usage scenarios, Processor 2900 is enabled tooptionally and/or selectively perform stochastic rounding forfloating-point operations that produce integer results or fixed-pointresults. For example, Processor 2900 is enabled to perform stochasticrounding for a floating-point to integer conversion operation, with thestochastic rounding affecting the resultant integer value. For anotherexample, Processor 2900 is enabled to perform stochastic rounding for afloating-point to fixed-point conversion operation, with the stochasticrounding affecting the resultant fixed-point value.

In various embodiments and/or usage scenarios, the training process withFP computations that form long dependency chains correspondsconceptually and/or is related conceptually to concepts disclosed insection “Deep Learning Accelerator Example Uses” (see, e.g., FIGS.27A-28E and related text) and section “Example Workload Mapping andExemplary Tasks” (see, e.g., FIGS. 11-12 and related text). For example,First Forward Pass 2711 of FIG. 27A, Forward Pass 2751 of FIG. 27C, andForward Pass 2771 of FIG. 27D respectively correspond to FP computationswith long dependency chains. For another example, f_psum:prop 1103 ofFIG. 11 corresponds to an element of a long dependency chain of FPcomputations.

In various embodiments and/or usage scenarios, all or any portions ofProcessor 2900 of FIG. 29 correspond and/or are related conceptually toall or any elements of a PE or a CE of a PE. For example, an instance ofProcessor 2900 corresponds to an instance of PE 499 of, e.g., FIG. 4.For another example, a two-dimensional array of instances of Processor2900 corresponds to the two-dimensional array of instances of PE 499interconnected as illustrated in FIG. 4. For another example, Processor2900 corresponds to CE 800 of FIG. 8. For another example, all or anyportions of FPU 2901 correspond and/or are related conceptually tovarious elements of Data Path 852 of FIG. 8. For another example, all orany portions of Instruction Decode Logic 2920 correspond or are relatedconceptually to elements of Dec 840 of FIG. 8. For another example, allor any potions of FP Control Register 2925 are implemented in CE 800.For another example, all or any portions of RNGs 2921 correspond and/orare related conceptually to various elements of Data Path 852. Invarious embodiments and/or usage scenarios, one or more instances ofInstruction 2950 are stored in memory 854 of FIG. 8.

In various embodiments and/or usage scenarios, one or more instances ofInstruction 2950 correspond to all or any portions of Task SW on PEs 260of FIG. 2, and/or correspond to all or any portions of Forward Pass,Delta Pass, Chain Pass, Update Weights 350 of FIG. 3. In variousembodiments and/or usage scenarios, all or any portions of actionsillustrated in FIG. 31 correspond to all or any portions of ExecuteFetched Instruction(s) 906 of FIG. 9A.

In various embodiments and/or usage scenarios, all or any portions ofInstruction 2950 correspond and/or are related conceptually toinstructions, e.g., Multiple Operand Instruction 2510 of FIG. 25A, OneSource, No Destination Operand Instruction 2520 of FIG. 25B, andImmediate Instruction 2530 of FIG. 25C. For example, OpCode Bits 3023corresponds to Opcode 2512 of FIG. 25A. For another example, Source Bits3024 corresponds to Operand 0 Encoding 2513 of FIG. 25A. For anotherexample, Dest Bits 3025 corresponds to Operand 0 Encoding 2513 of FIG.25A. For another example, Rounding Mode Bits 3021 is determinable fromOperand 1 Encoding 2514 of FIG. 25A.

FIG. 32 illustrates a flow diagram of selected details of an embodimentof floating-point processing in accordance with a programmable exponentbias, such as in a context of Processor 2900. Flow begins (Start 3200)by programming an exponent bias to use for subsequent floating-pointcomputations (Program Exponent Bias 3201), such as by executing aninstruction to set Exponent Bias 2925.6 of FIG. 30B to the exponentbias. Then zero or more floating-point computations are performed inaccordance with the programmed exponent bias (Perform Computation(s)3202), such as by Processor 2900 performing the floating-pointcomputations in response to zero or more corresponding floating-pointinstructions. After the zero or more floating-point computations areperformed, a test determines whether the programmable exponent bias isto be changed (Change Exponent Bias? 3203). If so (Yes 3205), then flowproceeds to program the programmable exponent bias with a differentvalue (Program Exponent Bias 3201). If not (No 3204), then furtherfloating-point computations are performed in accordance with thepreviously programmed programmable exponent value (PerformComputation(s) 3202).

In various embodiments and/or usage scenarios, Change Exponent Bias?3203 is one or more of: implied, unconditional, non-selective, static,and a-priori, e.g., a first portion of processing is a-priori to be inaccordance with a first exponent bias, and a second portion ofprocessing is a-priori to be in accordance with a second exponent bias.Other portions of processing are a-priori to be in accordance withrespective exponent biases, and so forth. For example, a first portionof processing is of neural network data that is not normalized, and afirst exponent bias is used. Continuing with the example, a secondportion of processing of neural network data that is normalized, and asecond exponent bias is used. In some circumstances, the first exponentbias is greater than the second exponent bias. In other circumstances,the first exponent bias is less than the second exponent bias. Invarious embodiments and/or usage scenarios, software (or a user)explicitly indicates that the data for computations are within a certainrange (e.g., the unit interval [0,1]) or that the data is normalized tothe average value.

In various embodiments and/or usage scenarios, Change Exponent Bias?3203 is one or more of: explicit, conditional, selective, dynamic, andnot a-priori, e.g., a determination is made that data is of relativelyhigh magnitudes and in response the exponent bias is adjusted downward,or alternatively the data is of relatively low magnitudes and inresponse the exponent bias is adjusted upward. In some circumstances,other operations are performed in conjunction with programming theexponent bias in Exponent Bias 2925.6 with a different value, such asadjusting, e.g., previously computed and/or stored floating-point valuesto be in accordance with the different value.

In various embodiments and/or usage scenarios, a first plurality of PEsis operated with a first programmable exponent bias set to a firstvalue, and a second plurality of PEs is operated with a secondprogrammable exponent bias set to a second value. In some circumstances,the operation of the first plurality of PEs is with respect to a firstneural network, and the operation of the second plurality of PEs is withrespect to a second neural network. In some circumstances, the operationof the first plurality of PEs and the operation of the second pluralityof PEs are with respect to a same neural network. In some circumstances,the operation of the first plurality of PEs is with respect to a firstportion of a neural network and the operation of the second plurality ofPEs is with respect to a second portion of the same neural network.

Scalability for Large Deep Neural Networks

A consideration in evaluating hardware architectures for implementingDeep Neural Networks (DNN) is storage capacity of the hardware incomparison to storage requirements for weights associated with the DNN.The weights are an example of a parameter of a neural network.Additional storage required for forward partial sums, activations(including but not limited to layer outputs), and other implementationoverhead (e.g. for convolutions), however, is in some situations, modestcompared to the storage requirements for the weights. In the context ofacademic and industrial benchmarks, popular DNNs include LeNet-5,AlexNet, VGG-16, GoogLeNet(v1), and ResNet-50. Some DNNs range from 4 to50 layers, require between 341K and 15.5G MAC (Multiply and Accumulate)operations, and require between 60K and 138M weights, in total acrossall layers. Assuming each weight requires 16-bit precision, the popularDNNs have storage requirements of between 120 KB and 276 MB, just forweights, after training. For 32-bit precision, the requirements aredouble. Additional storage is required during training, e.g., forgradient accumulations, delta partial sums, layer errors, and duplicatedweights. For some training methods (e.g., minibatch), the weights areduplicated multiple times, increasing the weight storage requirementsaccordingly.

Various factors affect usage of memory of a hardware accelerator fordeep neural networks, e.g., Memory 854 of FIG. 8, between instructionsand data, and further between the various types of data, e.g. weights,gradient accumulations, forward partial sums, delta partial sums, andforward pass activations. E.g., the various factors include the dataflowgraph being executed and the particular algorithms used. In variousembodiments and/or usage scenarios, with respect to the PE comprisingit, Memory 854 provides a private memory space with unified storage forneuron inputs, neuron outputs, and synaptic weights for neuron(s) mappedto the PE. It is understood, that for convolution layers, the termneuron represents a filter or kernel. In various embodiments and/orusage scenarios, there are 500K PEs in which Memory 854 holds 48 KB,with 16 KB used for instructions and 32 KB used for data per PE, for 24GB total memory. Further according to embodiment there are, e.g.,between 20K and 40K PEs per ASIC, and each ASIC holds between 0.96 and1.92 GB, with between 0.24 and 0.48 GB used for instructions and between0.72 and 1.44 GB used for data per ASIC. In various embodiments and/orusage scenarios, there are 3M PEs in which Memory 854 holds 8 KB, with 2KB used for instructions and 6 KB used for data per PE, for 24 GB totalmemory. Further according to embodiment there are, e.g., between 20K and40K PEs per ASIC, and each ASIC holds between 0.16 and 0.32 GB, withbetween 0.04 and 0.08 GB used for instructions and between 0.12 and 0.24GB used for data per ASIC.

Using either 16-bit or 32-bit precision weights, any of theaforementioned embodiments, in which Memory 854 holds 48 KB, is enabledto minimally implement the most demanding (VGG-16) of the abovementioned popular DNNs in a single ASIC, with all layers concurrentlyresident, for one or both of inference and training (e.g., for one orboth of forward propagation and backward propagation), and without usingexternal check-pointing or other external (off chip, or off wafer)storage of any of the intermediate (not yet final) state of the DNN. Anyof the aforementioned embodiments, in which Memory 854 holds 8 KB ormore, is enabled to minimally implement any of the above mentionedpopular DNNs across a small plurality of ASICs of the wafer, with alllayers concurrently resident, for one or both of inference and training,and without using external check-pointing or other external (off chip,or off wafer) storage of any of the intermediate state of the DNN. Therequired minimum number of ASICs depends on the embodiment (e.g., 8 KBvs. 48 KB for Memory 854, and e.g., whether weights of 16-bit or 32-bitprecision are used). Stated differently, all (e.g., 100%) of the neuronsand synapses of large DNNs are implementable in hardware (moreparticularly, in wafer 412, of Deep Learning Accelerator 400, of FIG.4), with all layers (input, hidden (aka intermediate), and output)concurrently resident and executing, for one or both of inference andtraining, and without using external check-pointing or other external(off chip, or off wafer) storage of any of the intermediate (not yetfinal) state of the DNN.

In various embodiments and/or usage scenarios, Data Path 852 of FIG. 8includes respective dedicated hardware resources for floating-pointmultiply, format conversion, addition, shifting, and logic. In variousembodiments and/or usage scenarios, Data Path 852 implementshalf-precision (16-bit) and single-precision (32-bit) IEEE-754floating-point using a half-precision multiplier. In various embodimentsand/or usage scenarios, Data Path 852 comprises an 11×11 multiplierarray, an 8×8 multiplier array, a 22-bit adder, a 16-bit adder, a 22-bitshifter, and a 16-bit logic unit. Further according to embodiment thereare, e.g., between 500K and 3M PEs per wafer, corresponding to between500K and 3M instances of Data Path 852 and, except for defects, acorresponding number of multipliers, adders, shifters, and logic unitsper wafer. Further according to embodiment there are, e.g., between 20Kand 40K PEs per ASIC, corresponding to between 20K and 40K instances ofData Path 852 and, except for defects, a corresponding number ofmultipliers, adders, shifters, and logic units per ASIC.

As described above, the aforementioned embodiments, in which Memory 854holds between 8 KB and 48 KB, are enabled to minimally implement any ofthe above-mentioned popular DNNs via a small plurality of ASICs of thewafer. However, in view of the large number of MAC operations requiredfor large DNNs (e.g., 15.5G MAC operations for VGG-16), performance(often viewed in terms of “wall-clock time”) for minimal implementationsof such large DNNs is constrained by the number of data path resources,particularly multipliers, which for various embodiments and/or usagescenarios are necessarily being reused. Yet, according to embodiment,the entire system will have 500K to 3M instances of Data Path 852, or25× to 150× the number as a single ASIC. By smearing (as discussed indetail elsewhere herein) and/or spreading out the neurons of the DNN(across more PEs and more ASICS of the wafer, but mindful of transferlatencies between the spread neurons) will offer potential speedup (andcorresponding reduced wall-clock time) via enabling increased concurrentuse, particularly of multipliers. Stated differently, in variousembodiments and/or usage scenarios, in executing the training and/oroperation of a dataflow graph (e.g. a DNN), the system is enabled toscale the performance (e.g., reduce wall-clock time) by one to twoorders of magnitude (potentially, e.g., 25× to 150×, according toembodiment) by altering the placement (the mapping of the DNN onto PEs)to change utilization (e.g., increase parallel operation of greaternumbers of multipliers) of the large number of instances of Data Path852 in Deep Learning Accelerator 400 (e.g., via selective spreadingand/or smearing of the nodes of the dataflow graph, or the neurons ofthe DNN).

Other Embodiment Details

Embodiments and usage scenarios described with respect to FIGS. 1-31 areconceptually with respect to a PE comprising a CE that is programmable,e.g., that processes data according to instructions. Other embodimentsare contemplated with one or more of the CEs being partially or entirelyhardwired, e.g., that process data according to one or morefixed-circuit processing elements operable without instructions. As aspecific example, a particular CE comprises a hardware logic unitcircuit that implements all or a portion of an LSTM unit. The particularCE is comprised with a router in a particular PE that is operable in afabric with other PEs. Some of the other PEs are similar to or identicalto the particular PE and some of the other PEs are similar to oridentical to PE 499 of, e.g., FIG. 4.

Example Implementation Techniques

In some embodiments, various combinations of all or any portions ofoperations performed for and/or structure associated with any ofaccelerated deep learning; numerical representation for neural networks;stochastic rounding for accelerated deep learning; data structuredescriptors and fabric vectors for accelerated deep learning; neuronsmearing for accelerated deep learning; microthreading for accelerateddeep learning; task activating for accelerated deep learning;backpressure for accelerated deep learning; task synchronization foraccelerated deep learning; dataflow triggered tasks for accelerated deeplearning; a control wavelet for accelerated deep learning; a waveletrepresentation for accelerated deep learning; and/or continuouspropagation for accelerated deep learning; as well as portions of aprocessor, microprocessor, system-on-a-chip,application-specific-integrated-circuit, hardware accelerator, or othercircuitry providing all or portions of the aforementioned operations,are specified by a specification compatible with processing by acomputer system. The specification is in accordance with variousdescriptions, such as hardware description languages, circuitdescriptions, netlist descriptions, mask descriptions, or layoutdescriptions. Example descriptions include: Verilog, VHDL, SPICE, SPICEvariants such as PSpice, IBIS, LEF, DEF, GDS-II, OASIS, or otherdescriptions. In various embodiments, the processing includes anycombination of interpretation, compilation, simulation, and synthesis toproduce, to verify, or to specify logic and/or circuitry suitable forinclusion on one or more integrated circuits. Each integrated circuit,according to various embodiments, is compatible with design and/ormanufacture according to a variety of techniques. The techniques includea programmable technique (such as a field or mask programmable gatearray integrated circuit), a semi-custom technique (such as a wholly orpartially cell-based integrated circuit), and a full-custom technique(such as an integrated circuit that is substantially specialized), anycombination thereof, or any other technique compatible with designand/or manufacture of integrated circuits.

In some embodiments, various combinations of all or portions ofoperations as described by a computer readable medium having a set ofinstructions stored therein, are performed by execution and/orinterpretation of one or more program instructions, by interpretationand/or compiling of one or more source and/or script languagestatements, or by execution of binary instructions produced bycompiling, translating, and/or interpreting information expressed inprogramming and/or scripting language statements. The statements arecompatible with any standard programming or scripting language (such asC, C++, Fortran, Pascal, Ada, Java, VBscript, and Shell). One or more ofthe program instructions, the language statements, or the binaryinstructions, are optionally stored on one or more computer readablestorage medium elements. In various embodiments, some, all, or variousportions of the program instructions are realized as one or morefunctions, routines, sub-routines, in-line routines, procedures, macros,or portions thereof.

CONCLUSION

Certain choices have been made in the description merely for conveniencein preparing the text and drawings, and unless there is an indication tothe contrary, the choices should not be construed per se as conveyingadditional information regarding structure or operation of theembodiments described. Examples of the choices include: the particularorganization or assignment of the designations used for the figurenumbering and the particular organization or assignment of the elementidentifiers (the callout5 or numerical designators, e.g.) used toidentify and reference the features and elements of the embodiments.

Various forms of the words “include” and “comprise” are specificallyintended to be construed as abstractions describing logical sets ofopen-ended scope and are not meant to convey physical containment unlessdescribed explicitly (such as followed by the word “within”).

Language in the claims or elsewhere herein of the form of “at least oneof A, . . . , and N”, “one or more of A, . . . , and N”, or “anycombination of A, . . . , and N” are to be construed to mean “one ormore selected from the group of A, . . . , and N” (where ellipsisindicates an arbitrary plurality of group members). Furthermore, withoutexpress indication to the contrary, such language is not meant to closean otherwise open-ended group (e.g., a claim or a claim element).

Although the foregoing embodiments have been described in some detailfor purposes of clarity of description and understanding, the inventionis not limited to the details provided. There are many embodiments ofthe invention. The disclosed embodiments are exemplary and notrestrictive.

It will be understood that many variations in construction, arrangement,and use are possible consistent with the description, and are within thescope of the claims of the issued patent. For example, interconnect andfunction-unit bit-widths, clock speeds, and the type of technology usedare variable according to various embodiments in each component block.The names given to interconnect and logic are merely exemplary, andshould not be construed as limiting the concepts described. The orderand arrangement of flowchart and flow diagram process, action, andfunction elements are variable according to various embodiments. Also,unless specifically stated to the contrary, value ranges specified,maximum and minimum values used, or other particular specifications(such as file types; and the number of entries or stages in registersand buffers), are merely those of the described embodiments, areexpected to track improvements and changes in implementation technology,and should not be construed as limitations.

Functionally equivalent techniques known in the art are employableinstead of those described to implement various components, sub-systems,operations, functions, routines, sub-routines, in-line routines,procedures, macros, or portions thereof. It is also understood that manyfunctional aspects of embodiments are realizable selectively in eitherhardware (e.g., generally dedicated circuitry) or software (e.g., viasome manner of programmed controller or processor), as a function ofembodiment dependent design constraints and technology trends of fasterprocessing (facilitating migration of functions previously in hardwareinto software) and higher integration density (facilitating migration offunctions previously in software into hardware). Specific variations invarious embodiments include, but are not limited to: differences inpartitioning; different form factors and configurations; use ofdifferent operating systems and other system software; use of differentinterface standards, network protocols, or communication links; andother variations to be expected when implementing the concepts describedherein in accordance with the unique engineering and businessconstraints of a particular application.

The embodiments have been described with detail and environmentalcontext well beyond that required for a minimal implementation of manyaspects of the embodiments described. Those of ordinary skill in the artwill recognize that some embodiments omit disclosed components orfeatures without altering the basic cooperation among the remainingelements. It is thus understood that much of the details disclosed arenot required to implement various aspects of the embodiments described.To the extent that the remaining elements are distinguishable from theprior art, components and features that are omitted are not limiting onthe concepts described herein.

All such variations in design are insubstantial changes over theteachings conveyed by the described embodiments. It is also understoodthat the embodiments described herein have broad applicability to othercomputing and networking applications, and are not limited to theparticular application or industry of the described embodiments. Theinvention is thus to be construed as including all possiblemodifications and variations encompassed within the scope of the claimsof the issued patent.

1. (canceled)
 2. An apparatus comprising: a programmable processorenabled to execute instructions comprising floating-point instructions;exponent bias and exponent size control resources of the programmableprocessor enabled to maintain respective state representing a currentexponent bias of a plurality of exponent biases and a current exponentsize of a plurality of exponent sizes; a floating-point unit of theprogrammable processor enabled to perform floating-point operationscorresponding to the floating-point instructions and in accordance withfloating-point operands and floating-point results each comprising arespective biased exponent, the floating-point operations interpretingthe biased exponents of the floating-point operands and producing thebiased exponents of the floating-point results in accordance with thecurrent exponent bias and the current exponent size; and wherein theinstructions further comprise first and second load control resourceinstructions for respective operations to update the respective statemaintained in the exponent bias and exponent size control resources. 3.The apparatus of claim 2, wherein: the exponent size of the exponentsize control resource specifies one of a plurality of floating-pointformats, a first of the plurality of floating-point formats comprising abiased exponent of N bits and a mantissa of M bits, and a second of theplurality of floating-point formats comprising a biased exponent of(N+delta) bits and a mantissa of (M−delta) bits.
 4. The apparatus ofclaim 2, wherein the programmable processor is one of a plurality oflike programmable processors fabricated on a wafer in accordance withwafer-scale integration, and a datacenter element enabled to performneural network processing comprises the wafer.
 5. The apparatus of claim2, wherein the programmable processor comprises one or more hardwareregisters comprising at least one of the exponent bias and the exponentsize control resources, and at least one of the first and the secondload control resource instructions is a load hardware registerinstruction.
 6. The apparatus of claim 2, wherein at least one of theexponent bias and the exponent size control resources is memory-mapped,and at least one of the first and the second load control resourceinstructions is a memory store instruction.
 7. The apparatus of claim 2,wherein: the instructions further comprise a third load control resourceinstruction, the apparatus further comprises a rounding mode controlresource of the programmable processor enabled to maintain staterepresenting a current rounding mode of a plurality of rounding modes,at least one of the rounding modes comprising saturating thefloating-point results to a value comprising a maximum biased exponent,and the floating-point unit is further enabled to perform thefloating-point operations in accordance with the current rounding mode.8. The apparatus of claim 2, wherein: the instructions further comprisea third load control resource instruction, the apparatus furthercomprises a flush-to-zero control resource of the programmable processorenabled to maintain state representing a current flush-to-zero mode of aplurality of flush-to-zero modes, at least one of the flush-to-zeromodes comprising flushing subnormal results to zero, and thefloating-point unit is further enabled to perform the floating-pointoperations in accordance with the current flush-to-zero mode.
 9. Anapparatus comprising: a programmable processor enabled to executeinstructions comprising floating-point instructions; exponent bias andmaximum biased exponent normal control resources of the programmableprocessor enabled to maintain respective state representing a currentexponent bias of a plurality of exponent biases and a current maximumbiased exponent normal specifying one of a plurality of maximum biasedexponent modes, the maximum biased exponent modes comprising a firstmode using a maximum biased exponent to represent a biased exponent; afloating-point unit of the programmable processor enabled to performfloating-point operations corresponding to the floating-pointinstructions and in accordance with floating-point operands andfloating-point results each comprising a respective biased exponent, thefloating-point operations interpreting the biased exponents of thefloating-point operands and producing the biased exponents of thefloating-point results in accordance with the current exponent bias andthe current maximum biased exponent normal; and wherein the instructionsfurther comprise first and second load control resource instructions forrespective operations to update the respective state maintained in theexponent bias and maximum biased exponent normal control resources. 10.The apparatus of claim 9, wherein: the instructions further comprise athird load control resource instruction, the apparatus further comprisesa rounding mode control resource of the programmable processor enabledto maintain state representing a current rounding mode of a plurality ofrounding modes, at least one of the rounding modes comprising saturatingthe floating-point results to a value comprising a maximum biasedexponent, and the floating-point unit is further enabled to perform thefloating-point operations in accordance with the current rounding mode.11. The apparatus of claim 9, wherein the maximum biased exponent modesfurther comprise a second mode using the maximum biased exponent torepresent at least one of: infinite numbers and IEEE compatible NaN. 12.The apparatus of claim 9, wherein the programmable processor is one of aplurality of like programmable processors fabricated on a wafer inaccordance with wafer-scale integration, and a datacenter elementenabled to perform neural network processing comprises the wafer. 13.The apparatus of claim 9, wherein the programmable processor comprisesone or more hardware registers comprising at least one of the exponentbias and the maximum biased exponent normal control resources, and atleast one of the first and the second load control resource instructionsis a load hardware register instruction.
 14. The apparatus of claim 9,wherein at least one of the exponent bias and the maximum biasedexponent normal control resources is memory-mapped, and at least one ofthe first and the second load control resource instructions is a memorystore instruction.
 15. An apparatus comprising: a programmable processorenabled to execute instructions comprising floating-point instructions;exponent bias and zero biased exponent mode control resources of theprogrammable processor enabled to maintain respective state representinga current exponent bias of a plurality of exponent biases and a zerobiased exponent mode of a plurality of zero biased exponent modes, thezero biased exponent modes comprising a first mode using a zero biasedexponent to represent a biased exponent; a floating-point unit of theprogrammable processor enabled to perform floating-point operationscorresponding to the floating-point instructions and in accordance withfloating-point operands and floating-point results each comprising arespective biased exponent, the floating-point operations interpretingthe biased exponents of the floating-point operands and producing thebiased exponents of the floating-point results in accordance with thecurrent exponent bias and the current zero biased exponent mode; andwherein the instructions further comprise first and second load controlresource instructions for respective operations to update the respectivestate maintained in the exponent bias and zero biased exponent modecontrol resources.
 16. The apparatus of claim 15, wherein: theinstructions further comprise a third load control resource instruction,the apparatus further comprises a flush-to-zero control resource of theprogrammable processor enabled to maintain state representing a currentflush-to-zero mode of a plurality of flush-to-zero modes, at least oneof the flush-to-zero modes comprising flushing subnormal results tozero, and the floating-point unit is further enabled to perform thefloating-point operations in accordance with the current flush-to-zeromode.
 17. The apparatus of claim 15, wherein the zero biased exponentmodes further comprise a second mode using the zero biased exponent torepresent at least subnormal floating-point numbers.
 18. The apparatusof claim 15, wherein the programmable processor is one of a plurality oflike programmable processors fabricated on a wafer in accordance withwafer-scale integration, and a datacenter element enabled to performneural network processing comprises the wafer.
 19. The apparatus ofclaim 15, wherein the programmable processor comprises one or morehardware registers comprising at least one of the exponent bias and thezero biased exponent mode control resources, and at least one of thefirst and the second load control resource instructions is a loadhardware register instruction.
 20. The apparatus of claim 15, wherein atleast one of the exponent bias and the zero biased exponent mode controlresources is memory-mapped, and at least one of the first and the secondload control resource instructions is a memory store instruction. 21.The apparatus of claim 15, wherein: the instructions further comprise athird load control resource instruction, the apparatus further comprisesa rounding mode control resource of the programmable processor enabledto maintain state representing a current rounding mode of a plurality ofrounding modes, at least one of the rounding modes comprising saturatingthe floating-point results to a value comprising a maximum biasedexponent, and the floating-point unit is further enabled to perform thefloating-point operations in accordance with the current rounding mode.22. A method comprising: maintaining respective state in exponent biasand exponent size control resources of a programmable processor, therespective state representing a current exponent bias of a plurality ofexponent biases and a current exponent size of a plurality of exponentsizes; executing instructions by the programmable processor, theinstructions comprising floating-point instructions and first and secondload control resource instructions; performing floating-point operationsby a floating-point unit of the programmable processor, thefloating-point operations corresponding to the floating-pointinstructions and in accordance with floating-point operands andfloating-point results each comprising a respective biased exponent, thefloating-point operations interpreting the biased exponents of thefloating-point operands and producing the biased exponents of thefloating-point results in accordance with the current exponent bias andthe current exponent size; and updating the state maintained in theexponent bias and exponent size control resources via operations by thefirst and second load control resource instructions.
 23. The method ofclaim 22, wherein: the exponent size of the exponent size controlresource specifies one of a plurality of floating-point formats, a firstof the plurality of floating-point formats comprising a biased exponentof N bits and a mantissa of M bits, and a second of the plurality offloating-point formats comprising a biased exponent of (N+delta) bitsand a mantissa of (M−delta) bits.
 24. The method of claim 22, whereinthe programmable processor is one of a plurality of like programmableprocessors fabricated on a wafer in accordance with wafer-scaleintegration, and a datacenter element enabled to perform neural networkprocessing comprises the wafer.
 25. The method of claim 22, wherein theprogrammable processor comprises one or more hardware registerscomprising at least one of the exponent bias and the exponent sizecontrol resources, and at least one of the first and the second loadcontrol resource instructions is a load hardware register instruction.26. The method of claim 22, wherein at least one of the exponent biasand the exponent size control resources is memory-mapped, and at leastone of the first and the second load control resource instructions is amemory store instruction.
 27. The method of claim 22, furthercomprising: maintaining rounding mode state in a rounding mode controlresource of the programmable processor, the rounding mode staterepresenting a current rounding mode of a plurality of rounding modes,at least one of the rounding modes comprising saturating thefloating-point results to a value comprising a maximum biased exponent;and wherein the instructions further comprise a third load controlresource instruction, and the floating-point unit is further enabled toperform the floating-point operations in accordance with the currentrounding mode.
 28. The method of claim 22, further comprising:maintaining flush-to-zero mode state in a flush-to-zero mode controlresource of the programmable processor, the flush-to-zero mode staterepresenting a current flush-to-zero mode of a plurality offlush-to-zero modes, at least one of the flush-to-zero modes comprisingflushing subnormal results to zero; and wherein the instructions furthercomprise a third load control resource instruction, and thefloating-point unit is further enabled to perform the floating-pointoperations in accordance with the current flush-to-zero mode.
 29. Themethod of claim 22, wherein a distinct change in the state maintained inthe exponent bias and exponent size control resources is interposedbetween the sequential executing of each of two floating-pointinstructions of the floating-point instructions, and wherein the twofloating-point instructions are distinct instances of a samefloating-point instruction.
 30. The method of claim 22, wherein adistinct change in the state maintained in the exponent bias andexponent size control resources is interposed between the sequentialexecuting of each of two floating-point instructions of thefloating-point instructions, and wherein the two floating-pointinstructions are respectively responsive to respective fetching of asame instruction from a same memory location.
 31. The method of claim22, wherein a distinct change in the state maintained in the exponentbias and exponent size control resources is interposed between thesequential executing of each of two floating-point instructions of thefloating-point instructions, and wherein the two floating-pointinstructions are respectively responsive to respective fetching ofidentically coded instructions from respective different memorylocations.
 32. The method of claim 22, wherein a distinct change in thestate maintained in the exponent bias and exponent size controlresources is interposed between the sequential executing of each of twoportions of the floating-point instructions, and wherein at least oneinstruction from each of the two portions has a same instruction opcodevalue.
 33. The method of claim 22, wherein a distinct change in thestate maintained in the exponent bias and exponent size controlresources is interposed between the sequential executing of each of twoportions of the floating-point instructions, and wherein at least oneinstruction from each of the two portions are respective instances of afloating-point multiply-accumulate instruction.
 34. The method of claim22, wherein the executing of one of the first and second load controlresource instructions is one of: unconditional, non-selective, static,and a-priori determined.
 35. The method of claim 22, wherein theexecuting of one of the first and second load control resourceinstructions is one of: conditional, selective, dynamic, and nota-priori determined.